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In this paper we consider the problem of detecting blurred regions in high-resolution whole slide histologic images. The proposed method is based on the use of a Fourier neural operator trained on the results of two simultaneously used approaches: blur detection using multiscale analysis of the discrete cosine transform coefficients and estimation of the degree of sharpness of objects edges in the image. The efficiency of the algorithm is confirmed on images from the datasets PATH-DT-MSU [1] and FocusPath [2]. [ABSTRACT FROM AUTHOR]

In recent years, there has been extensive research on object detection methods applied to optical remote sensing images utilizing convolutional neural networks. Despite these efforts, the detection of small objects in remote sensing remains a formidable challenge. The deep network structure will bring about the loss of object features, resulting in the loss of object features and the near elimination of some subtle features associated with small objects in deep layers. Additionally, the features of small objects are susceptible to interference from background features contained within the image, leading to a decline in detection accuracy. Moreover, the sensitivity of small objects to the bounding box perturbation further increases the detection difficulty. In this paper, we introduce a novel approach, Cross-Layer Fusion and Weighted Receptive Field-based YOLO (CAW-YOLO), specifically designed for small object detection in remote sensing. To address feature loss in deep layers, we have devised a cross-layer attention fusion module. Background noise is effectively filtered through the incorporation of Bi-Level Routing Attention (BRA). To enhance the model's capacity to perceive multi-scale objects, particularly small-scale objects, we introduce a weightedmulti-receptive field atrous spatial pyramid poolingmodule. Furthermore, wemitigate the sensitivity arising from bounding box perturbation by incorporating the joint Normalized Wasserstein Distance (NWD) and Efficient Intersection over Union (EIoU) losses. The efficacy of the proposedmodel in detecting small objects in remote sensing has been validated through experiments conducted on three publicly available datasets. The experimental results unequivocally demonstrate the model's pronounced advantages in small object detection for remote sensing, surpassing the performance of current mainstream models. [ABSTRACT FROM AUTHOR]

The problem of solving the equivalent resistance between two points for resistor networks has important significance in physics. This paper mainly changes and rewrites the formula for calculating the resistance between two points of an unconventional m × n cylindrical resistor network with a zero resistor axis and any two left and right boundaries. To enhance the efficiency of calculating the equivalent resistance between two points, Chebyshev polynomials and hyperbolic cosine functions are employed to represent the new formula. And in the inference process, the famous discrete cosine transform of the third kind (DCT-III) is used to process the matrix. We give the equivalent resistance formula for several special cases, and display them by a three-dimensional graph. Subsequently, the calculation efficiency of the original formula and the rewritten formula are compared. At the end of the paper, a heuristic algorithm suitable for robot path planning on cylindrical environment is proposed. [ABSTRACT FROM AUTHOR]

Accurate and reliable medium- and long-term load forecasting is crucial for the rational planning and operation of power systems. However, existing methods often struggle to accurately extract and capture long-term dependencies in load data, leading to poor predictive accuracy. Therefore, this paper proposes a medium- and long-term residential load forecasting method based on FEDformer, aiming to capture long-term temporal dependencies of load data in the frequency domain while considering factors such as electricity prices and temperature, ultimately improving the accuracy of medium- and long-term load forecasting. The proposed model employs Discrete Cosine Transform (DCT) for frequency domain transformation of time-series data to address the Gibbs phenomenon caused by the use of Discrete Fourier Transform (DFT) in FEDformer. Additionally, causal convolution and attention mechanisms are applied in the frequency domain to enhance the model's capability to capture long-term dependencies. The model is evaluated using real-world load data from power systems, and experimental results demonstrate that the proposed model effectively learns the temporal and nonlinear characteristics of load data. Compared to other baseline models, DCTformer improves prediction accuracy by 37.5% in terms of MSE, 26.9% in terms of MAE, and 26.24% in terms of RMSE. [ABSTRACT FROM AUTHOR]

Given the substantial value and considerable training costs associated with deep neural network models, the field of deep neural network model watermarking has come to the forefront. While black-box model watermarking has made commendable strides, the current methodology for constructing poisoned images in the existing literature is simplistic and susceptible to forgery. Notably, there is a scarcity of black-box model watermarking techniques capable of discerning a unique user in a multi-user model distribution setting. For this reason, this paper proposes a novel black-box model watermarking method for unique identity identification, which is denoted as the ID watermarking of neural networks (IDwNet). Specifically, to enhance the distinguishability of deep neural network models in multi-user scenarios and mitigate the likelihood of poisoned image counterfeiting, this study develops a discrete cosine transform (DCT) and singular value decomposition (SVD)-based symmetrical embedding method to form the poisoned image. As this ID embedding method leads to indistinguishable deep features, the study constructs a poisoned adversary training strategy by simultaneously inputting clean images, poisoned images with the correct ID, and poisoned adversary images with incorrect IDs to train a deep neural network. Extensive simulation experiments show that the proposed scheme achieves excellent invisibility for the concealed ID, surpassing remarkably the state-of-the-art. In addition, the proposed scheme obtains a validation success rate exceeding 99% for the poisoned images at the cost of a marginal classification accuracy reduction of less than 0.5%. Moreover, even though there is only a 1-bit discrepancy between IDs, the proposed scheme still results in an accurate validation of user copyright. These results indicate that the proposed scheme is promising. [ABSTRACT FROM AUTHOR]

Clustering holds significant utility across a spectrum of several domains, including pattern recognition, image analysis, customer analytics, market segmentation, social network analysis, and numerous other areas. The main advantages of image clustering are its degree of freedom regarding data labeling and the lack of training and model deployment, which makes them suitable for the overall study's purpose of land cover segmentation and deforestation monitoring. In previous work, the Gaussian Mixture Model (GMM) technique has been established as the best option. Due to the necessity of implementing the algorithm on light unmanned airborne platforms for fast deforestation monitoring, the high resources and long computation time became an issue. This paper proposes several cost-efficient GMM clustering algorithms based on discrete transforms traditionally used for image compression. The results will show that the proposed methods maintain the clustering output quality while drastically decreasing the computation time and also lowering the memory needed to perform. [ABSTRACT FROM AUTHOR]

The discrete cosine transform is a commonly used technique in the field of signal processing that employs cosine basis functions for signal analysis. Traditionally, the regression coefficients of the cosine basis functions are solely based on frequency information. This paper extends the regression coefficients associated with the cosine basis functions to take into account both frequency and time information, not just frequency information alone. This modification results in an ill-posed linear system, which requires regularization to prevent overfitting. To address this, this paper uses shaping regularization, a technique used to stabilize ill-posed problems. By doing so, the absolute values of these extended coefficients, now exhibiting variations in both frequency and time domains, are defined as the time–frequency distribution of that input signal. The numerical experiments conducted to validate this approach demonstrate that the proposed method yields a commendable time–frequency resolution. Consequently, it proves valuable for interpreting seismic data, showcasing its potential for applications in this field. [ABSTRACT FROM AUTHOR]

This paper introduces a method aiming at enhancing the efficacy of speaker identification systems within challenging acoustic environments characterized by noise and reverberation. The methodology encompasses the utilization of diverse feature extraction techniques, including Mel-Frequency Cepstral Coefficients (MFCCs) and discrete transforms, such as Discrete Cosine Transform (DCT), Discrete Sine Transform (DST), and Discrete Wavelet Transform (DWT). Additionally, an Artificial Neural Network (ANN) serves as the classifier for this method. Reverberation is modeled using varying-length comb filters, and its impact on pitch frequency estimation is explored via the Auto Correlation Function (ACF). This paper also contributes to the field of cancelable speaker identification in both open and reverberation environments. The proposed method depends on comb filtering at the feature level, deliberately distorting MFCCs. This distortion, incorporated within a cancelable framework, serves to obscure speaker identities, rendering the system resilient to potential intruders. Three systems are presented in this work; a reverberation-affected speaker identification system, a system depending on cancelable features through comb filtering, and a novel cancelable speaker identification system within reverbration environments. The findings revealed that, in both scenarios with and without reverberation effects, the DWT-based features exhibited superior performance within the speaker identification system. Conversely, within the cancelable speaker identification system, the DCT-based features represent the top-performing choice. [ABSTRACT FROM AUTHOR]

The Discrete Cosine Transform (DCT) can be used instead of the conventional Discrete Fourier Transform (DFT) for the Orthogonal Frequency Division Multiplexing (OFDM) construction, which offers many advantages. In this paper, the Multiple-Input-Multiple-Output (MIMO) DCT-OFDM is enhanced using a proposed Cosine Domain Equaliser (CDE) instead of a Frequency Domain Equaliser (FDE). The results are evaluated through the Rayleigh fading channel with Co-Carrier Frequency Offset (Co-CFO) of different MIMO configurations. The average bit error probability and the simulated time of the proposed scheme and the conventional one are compared, which indicates the importance of the proposed scheme. Also, a closed formula for the number of arithmetic operations of the proposed equaliser is developed. The proposed equaliser gives a simulation time reduction of about 81.21%, 83.74% compared to that of the conventional Linear Zero Forcing (LZF)-FDE and Linear Minimum Mean Square Error (LMMSE)-FDE, respectively, for the case of a 4 × 4 configuration. [ABSTRACT FROM AUTHOR]

Due to the advancement in biomedical technologies, to diagnose problems in people, a number of psychological signals are extracted from patients. We should be able to ensure that psychological signals are not altered by adversaries and it should be possible to relate a patient to his/her corresponding psychological signal. As far as our awareness extends, none of the existing methods possess the capability to both identify and verify the authenticity of the ECG signals. Consequently, this paper introduces an innovative dual-layer data-embedding approach for electrocardiogram (ECG) signals, aiming to achieve both signal identification and authenticity verification. Since file name-based signal identification is vulnerable to modifications, we propose a robust watermarking method which will embed patient-related details such as patient identification number, into the medically less-significant portion of the ECG signals. The proposed robust watermarking algorithm adds data into ECG signals such that the patient information hidden in an ECG signal can resist the filtering attack (such as high-pass filtering) and noise addition. This is achieved via the use of error buffers in the embedding algorithm. Further, modification-sensitive fragile watermarks are added to ECG signals. By extracting and checking the fragile watermark bits, we can determine whether an ECG signal is modified or not. To ensure the security of the proposed mechanism, two secret keys are used. Our evaluation demonstrates the usefulness of the proposed system. [ABSTRACT FROM AUTHOR]

The current 3D-ConvNet-based action recognition algorithms generally use GAP to compress feature information. However, it leads to issues of information loss, redundancy, and network overfitting. To address these issues and enhance the retention of high-level semantic information extracted by the convolutional layer, this paper proposed an action recognition al- gorithm based on GFDP. Firstly, DCT shows that GAP is a special case of feature decomposition in the frequency domain. Therefore, the algorithm introduced more frequency components to increase the specificity between feature channels and reduce the information redundancy after information compression. Secondly, to better suppress the overfitting problem, the algorithm introduced the batch normalization strategy to the convolutional layer and extended it to the fully connected layer of the action recognition model with ERB-Res3D as the skeleton to optimize the data distribution. Finally, this paper verified the proposed method on the UCF101 dataset. The results reveals that the model's computational load is 3.5 GFlops, with 7.4 million para- meters. The final recognition accuracy improved by 3.9% based on the ERB-Res3D model and 17.4% based on the original Res3D model. This improvement effectively achieves more accurate behavior recognition results. [ABSTRACT FROM AUTHOR]

Copyright of Forensic Science & Technology is the property of Institute of Forensic Science, Ministry of Public Security and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The discrete cosine transform (DCT) is widely used for image and video compression. Lossy algorithms such as JPEG, WebP, BPG and many others are based on it. Multiple modifications of DCT have been developed to improve its performance. One of them is adaptive DCT (ADCT) designed to deal with heterogeneous image structure and it may be found, for example, in the HEVC video codec. Adaptivity means that the image is divided into an uneven grid of squares: smaller ones retain information about details better, while larger squares are efficient for homogeneous backgrounds. The practical use of adaptive DCT algorithms is complicated by the lack of optimal threshold search algorithms for image partitioning procedures. In this paper, we propose a novel method for optimal threshold search in ADCT using a metric based on tonal distribution. We define two thresholds: pm, the threshold defining solid mean coloring, and ps, defining the quadtree fragment splitting. In our algorithm, the values of these thresholds are calculated via polynomial functions of the tonal distribution of a particular image or fragment. The polynomial coefficients are determined using the dedicated optimization procedure on the dataset containing images from the specific domain, urban road scenes in our case. In the experimental part of the study, we show that ADCT allows a higher compression ratio compared to non-adaptive DCT at the same level of quality loss, up to 66% for acceptable quality. The proposed algorithm may be used directly for image compression, or as a core of video compression framework in traffic-demanding applications, such as urban video surveillance systems. [ABSTRACT FROM AUTHOR]

Recent researches demonstrate that Deep Neural Networks (DNN) models are vulnerable to backdoor attacks. The backdoored DNN model will behave maliciously when images containing backdoor triggers arrive. To date, almost all the existing backdoor attacks are single-trigger and single-target attacks, and the triggers of most existing backdoor attacks are obvious thus are easy to be detected or noticed. In this paper, we propose a novel imperceptible and multi-channel backdoor attack method against Deep Neural Networks by exploiting Discrete Cosine Transform (DCT) steganography. The proposed method injects backdoor instances into the training set and does not require controlling the whole training process. Specifically, for a colored image, we utilize DCT steganography to construct and embed trigger into different channels of the image in frequency domain. As a result, the trigger shown in the time domain is stealthy and natural. Then the generated backdoor instances are injected into the training dataset to train the DNN model. Based on the proposed backdoor attack method, we implement two cunning variants of backdoor attacks, imperceptible N-to-N (multi-target) backdoor attack and imperceptible N-to-One (multi-trigger) backdoor attack. Experimental results demonstrate that the attack success rate of the N-to-N backdoor attack is 95.09% on CIFAR-10 dataset, 93.33% on TinyImageNet dataset and 92.45% on ImageNet dataset, respectively. The attack success rate of the N-to-One attack is 90.22% on CIFAR-10 dataset, 89.56% on TinyImageNet dataset and 88.29% on ImageNet dataset, respectively. Meanwhile, the proposed backdoor attack does not affect the classification accuracy of the DNN models. Moreover, the proposed attack is demonstrated to be robust against two state-of-the-art backdoor defenses, including the recent frequency domain defense. [ABSTRACT FROM AUTHOR]

Present age is the age of technology where we are all flooded with digital data. As the use of data is increasing day by day, the techniques and algorithms for data manipulation are also increasing at a rapid rate. The advancement in technology has brought about a revolution which has led to identification of various ways and techniques for modification and enhancement of images. On the other hand it also brings various challenges with respect to the prevention of tampering, forging or misuse of electronic data in form of image, audio or video. Digital watermarking can prove to be a very useful technique in prevention of any such scenario. Verifying the integrity of delicate images, such as medical photos, is crucial since even the smallest distortion or alteration might lead to a wrong diagnosis. Especially in digital images, watermarking techniques give protection from any such attack to a large extent. This research paper proposes a novel approach to digital image watermarking using a combination of Fibonacci scrambling, rotational embedding, and the Discrete Cosine Transform (DCT). The objective of the proposed algorithm is to bolster the security and resilience of the watermarking process through the incorporation of supplementary encryption and obfuscation mechanisms. We have implemented the code for this algorithm in Python. [ABSTRACT FROM AUTHOR]

Visual question answering (VQA) is a pivotal topic at the intersection of computer vision and natural language processing. This paper addresses the challenges of linguistic bias and bias fusion within invalid regions encountered in existing VQA models due to insufficient representation of multi-modal features. To overcome those issues, we propose a multi-feature enhancement scheme. This scheme involves the fusion of one or more features with the original ones, incorporating discrete cosine transform (DCT) features into the counterfactual reasoning framework. This approach harnesses finegrained information and spatial relationships within images and questions, enabling a more refined understanding of the indirect relationship between images and questions. Consequently, it effectively mitigates linguistic bias and bias fusion within invalid regions in the model. Extensive experiments are conducted on multiple datasets, including VQA2 and VQA-CP2, employing various baseline models and fusion techniques, resulting in promising and robust performance. [ABSTRACT FROM AUTHOR]

In the field of image watermarking technology, it is very important to balance imperceptibility, robustness and embedding capacity. In order to solve this key problem, this paper proposes a new color image adaptive watermarking scheme based on discrete wavelet transform (DWT), discrete cosine transform (DCT) and singular value decomposition (SVD). In order to improve the security of the watermark, we use Lorenz hyperchaotic mapping to encrypt the watermark image. We adaptively determine the embedding factor by calculating the Bhattacharyya distance between the cover image and the watermark image, and combine the Alpha blending technique to embed the watermark image into the Y component of the YCbCr color space to enhance the imperceptibility of the algorithm. The experimental results show that the average PSNR of our scheme is 45.9382 dB, and the SSIM is 0.9986. Through a large number of experimental results and comparative analysis, it shows that the scheme has good imperceptibility and robustness, indicating that we have achieved a good balance between imperceptibility, robustness and embedding capacity. [ABSTRACT FROM AUTHOR]

Lossy compression of remote-sensing images is a typical stage in their processing chain. In design or selection of methods for lossy compression, it is commonly assumed that images are noise-free. Meanwhile, there are many practical situations where an image or a set of its components are noisy. This fact needs to be taken into account since noise presence leads to specific effects in lossy compressed data. The main effect is the possible existence of the optimal operation point (OOP) shown for JPEG, JPEG2000, some coders based on the discrete cosine transform (DCT), and the better portable graphics (BPG) encoder. However, the performance of such modern coders as AVIF and HEIF with application to noisy images has not been studied yet. In this paper, analysis is carried out for the case of additive white Gaussian noise. We demonstrate that OOP can exist for AVIF and HEIF and the performance characteristics in it are quite similar to those for the BPG encoder. OOP exists with a higher probability for images of simpler structure and/or high-intensity noise, and this takes place according to different metrics including visual quality ones. The problems of providing lossy compression by AVIF or HEIF are shown and an initial solution is proposed. Examples for test and real-life remote-sensing images are presented. [ABSTRACT FROM AUTHOR]

Transformer has emerged as one of the modern neural networks that has been applied in numerous applications. However, transformers' large and deep architecture makes them computationally and memory-intensive. In this paper, we propose the block g-circulant matrices to replace the dense weight matrices in the feedforward layers of the transformer and leverage the DCT-DST algorithm to multiply these matrices with the input vector. Our test using Portuguese-English datasets shows that the suggested method improves model memory efficiency compared to the dense transformer but at the cost of a slight drop in accuracy. We found that the model Dense-block 1-circulant DCT-DST of 128 dimensions achieved the highest model memory efficiency at 22.14%. We further show that the same model achieved a BLEU score of 26.47%. [ABSTRACT FROM AUTHOR]

In response to the low matching accuracy of stereo matching algorithms in image regions with specular reflection, this paper proposes a multidimensional fusion stereo matching algorithm named MFANet. The algorithm embeds a multispectral attention module into the residual feature extraction network, utilizing two‐dimensional discrete cosine transforms to extract frequency features. In the pyramid pooling module, a coordinated attention mechanism is introduced to capture relevant positional information. In the cost aggregation part, the MFANet algorithm incorporates a three‐dimensional attention mechanism, focusing on the more important semantic information in high‐level features. By combining detailed information from low‐level features, semantic information from high‐level features, and contextual information, the algorithm generates features that are more conducive to disparity prediction. The MFANet algorithm is evaluated on three standard datasets (SceneFlow, KITTI2015, and KITTI2012). Experimental results demonstrate its robustness against specular reflection interference, accurate prediction of disparities in specular reflection pathological regions, and promising application prospects. [ABSTRACT FROM AUTHOR]

Copyright of Przegląd Elektrotechniczny is the property of Przeglad Elektrotechniczny and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper introduces a novel approach employing the fast cosine transform to tackle the 2-D and 3-D fractional nonlinear Schrödinger equation (fNLSE). The fractional Laplace operator under homogeneous Neumann boundary conditions is first defined through spectral decomposition. The difference matrix Laplace operator is developed by the second-order central finite difference method. Then, we diagonalize the difference matrix based on the properties of Kronecker products. The time discretization employs the Crank–Nicolson method. The conservation of mass and energy is proved for the fully discrete scheme. The advantage of this method is the implementation of the Fast Discrete Cosine Transform (FDCT), which significantly improves computational efficiency. Finally, the accuracy and effectiveness of the method are verified through two-dimensional and three-dimensional numerical experiments, solitons in different dimensions are simulated, and the influence of fractional order on soliton evolution is obtained; that is, the smaller the alpha, the lower the soliton evolution. [ABSTRACT FROM AUTHOR]

This paper aims to explore an alternative reversible digital watermarking solution to guarantee the integrity of and detect tampering with data of probative importance. Since the payload for verification is embedded in the contents, algorithms for reversible embedding and extraction, imperceptibility, payload capacity, and computational time are issues to evaluate. Thus, we propose a reversible and imperceptible audio information-hiding algorithm based on modified integer discrete cosine transform (intDCT) coefficient expansion. In this work, the original signal is segmented into fixed-length frames, and then intDCT is applied to each frame to transform signals from the time domain into integer DCT coefficients. Expansion is applied to DCT coefficients at a higher frequency to reserve hiding capacity. Objective evaluation of speech quality is conducted using listening quality objective mean opinion (MOS-LQO) and the segmental signal-to-noise ratio (segSNR). The audio quality of different frame lengths and capacities is evaluated. Averages of 4.41 for MOS-LQO and 23.314 [dB] for segSNR for 112 ITU-T test signals were obtained with a capacity of 8000 bps, which assured imperceptibility with the sufficient capacity of the proposed method. This shows comparable audio quality to conventional work based on Linear Predictive Coding (LPC) regarding MOS-LQO. However, all segSNR scores of the proposed method have comparable or better performance in the time domain. Additionally, comparing histograms of the normalized maximum absolute value of stego data shows a lower possibility of overflow than the LPC method. A computational cost, including hiding and transforming, is an average of 4.884 s to process a 10 s audio clip. Blind tampering detection without the original data is achieved by the proposed embedding and extraction method. [ABSTRACT FROM AUTHOR]

This paper emphasizes a faster digital processing time while presenting an accurate method for identifying spine fractures in X-ray pictures. The study focuses on efficiency by utilizing many methods that include picture segmentation, feature reduction, and image classification. Two important elements are investigated to reduce the classification time: Using feature reduction software and leveraging the capabilities of sophisticated digital processing hardware. The researchers use different algorithms for picture enhancement, including theWiener and Kalman filters, and they look into two background correction techniques. The article presents a technique for extracting textural features and evaluates three picture segmentation algorithms and three fractured spine detection algorithms using transformdomain, PowerDensity Spectrum(PDS), andHigher-Order Statistics (HOS) for feature extraction. With an emphasis on reducing digital processing time, this all-encompassing method helps to create a simplified system for classifying fractured spine fractures. A feature reduction program code has been built to improve the processing speed for picture classification. Overall, the proposed approach shows great potential for significantly reducing classification time in clinical settings where time is critical. In comparison to other transform domains, the texture features' discrete cosine transform (DCT) yielded an exceptional classification rate, and the process of extracting features from the transform domain took less time. More capable hardware can also result in quicker execution times for the feature extraction algorithms. [ABSTRACT FROM AUTHOR]

Recently, there have been several uses for digital image processing. Image fusion has become a prominent application in the domain of imaging processing. To create one final image that provesmore informative and helpful compared to the original input images, image fusion merges two or more initial images of the same item. Image fusion aims to produce, enhance, and transform significant elements of the source images into combined images for the sake of human visual perception. Image fusion is commonly employed for feature extraction in smart robots, clinical imaging, audiovisual camera integration, manufacturing process monitoring, electronic circuit design, advanced device diagnostics, and intelligent assembly line robots, with image quality varying depending on application. The research paper presents various methods for merging images in spatial and frequency domains, including a blend of stable and curvelet transformations, everageMax-Min, weighted principal component analysis (PCA), HIS (Hue, Intensity, Saturation), wavelet transform, discrete cosine transform (DCT), dual-tree Complex Wavelet Transform (CWT), and multiple wavelet transform. Image fusion methods integrate data from several source images of an identical target, thereby enhancing information in an extremely efficient manner. More precisely, in imaging techniques, the depth of field constraint precludes images from focusing on every object, leading to the exclusion of certain characteristics. To tackle thess challanges, a very efficient multi-focus wavelet decomposition and recompositionmethod is proposed. The use of these wavelet decomposition and recomposition techniques enables this method to make use of existing optimized wavelet code and filter choice. The simulated outcomes provide evidence that the suggested approach initially extracts particular characteristics from images in order to accurately reflect the level of clarity portrayed in the original images. This study enhances the performance of the eXtreme Gradient Boosting (XGBoost) algorithm in detecting brain malignancies with greater precision through the integration of computational image analysis and feature selection. The performance of images is improved by segmenting them employing the K-Means algorithm. The segmentation method aids in identifying specific regions of interest, using Particle Swarm Optimization (PCA) for trait selection and XGBoost for data classification. Extensive trials confirm the model's exceptional visual performance, achieving an accuracy of up to 97.067% and providing good objective indicators. [ABSTRACT FROM AUTHOR]

Colorectal cancer (CRC) exhibits a significant death rate that consistently impacts human lives worldwide. Histopathological examination is the standard method for CRC diagnosis. However, it is complicated, time-consuming, and subjective. Computer-aided diagnostic (CAD) systems using digital pathology can help pathologists diagnose CRC faster and more accurately than manual histopathology examinations. Deep learning algorithms especially convolutional neural networks (CNNs) are advocated for diagnosis of CRC. Nevertheless, most previous CAD systems obtained features from one CNN, these features are of huge dimension. Also, they relied on spatial information only to achieve classification. In this paper, a CAD system is proposed called "Color-CADx" for CRC recognition. Different CNNs namely ResNet50, DenseNet201, and AlexNet are used for end-to-end classification at different training–testing ratios. Moreover, features are extracted from these CNNs and reduced using discrete cosine transform (DCT). DCT is also utilized to acquire spectral representation. Afterward, it is used to further select a reduced set of deep features. Furthermore, DCT coefficients obtained in the previous step are concatenated and the analysis of variance (ANOVA) feature selection approach is applied to choose significant features. Finally, machine learning classifiers are employed for CRC classification. Two publicly available datasets were investigated which are the NCT-CRC-HE-100 K dataset and the Kather_texture_2016_image_tiles dataset. The highest achieved accuracy reached 99.3% for the NCT-CRC-HE-100 K dataset and 96.8% for the Kather_texture_2016_image_tiles dataset. DCT and ANOVA have successfully lowered feature dimensionality thus reducing complexity. Color-CADx has demonstrated efficacy in terms of accuracy, as its performance surpasses that of the most recent advancements. [ABSTRACT FROM AUTHOR]

Hyperspectral image (HSI) classification tasks have been adopted in huge applications of remote sensing recently. With the rise of deep learning development, it becomes crucial to investigate how to exploit spatial–spectral features. The traditional approach is to stack models that can encode spatial–spectral features, coupling sufficient information as much as possible, before the classification model. However, this sequential stacking tends to cause information redundancy. In this paper, a novel network utilizing the channel attention combined discrete cosine transform (DCTransformer) to extract spatial–spectral features has been proposed to address this issue. It consists of a detail spatial feature extractor (DFE) with CNN blocks and a base spectral feature extractor (BFE) utilizing the channel attention mechanism (CAM) with a discrete cosine transform (DCT). Firstly, the DFE can extract detailed context information using a series of layers of a CNN. Further, the BFE captures spectral features using channel attention and stores the wider frequency information by utilizing the DCT. Ultimately, the dynamic fusion mechanism has been adopted to fuse the detail and base features. Comprehensive experiments show that the DCTransformer achieves a state-of-the-art (SOTA) performance in the HSI classification task, compared to other methods on four datasets, the University of Houston (UH), Indian Pines (IP), MUUFL, and Trento datasets. On the UH dataset, the DCTransformer achieves an OA of 94.40%, AA of 94.89%, and kappa of 93.92. [ABSTRACT FROM AUTHOR]

This paper presents a new image encryption technique which can securely transmit images over unsecured networks. The security of transmitting information is the biggest point of concern in today's world. The proposed work utilizes Chaos and Arnold transform in the Discrete Cosine Stockwell Transform (DCST) domain for image encryption. For color images, a suitable color space transformation is employed to minimize the correlation among the RGB color planes. Further Sine-powered chaotic map-based confusion is applied on the less correlated planes, which are encrypted using the bands of DCST, sub-matrices permutation, and Arnold transformation. Encryption and decryption keys are generated from the number of bands in DCST, the period of Arnold transform, the arrangement of decomposed sub-matrices, and the initial seed used to generate the chaos sequence. To decrypt the image correctly, it is necessary to have all the keys in their original values and in the same order. The proposed method is compared with state of art methods and recent published papers, and the experimental results and security analysis has been performed to evaluate the performance of proposed work over existing state of art works and the results are found superior than the existing methods. [ABSTRACT FROM AUTHOR]

To overcome the drawbacks of the existing sinusoidal map and tent map, this paper proposes the design of a sinusoidal-tent (ST) map. The test results indicate that the new chaotic system exhibits more significant advantages in chaos control. Compared with the sinusoidal map and tent map, the proposed sinusoidal-tent map performs better in terms of bifurcation diagram and Lyapunov exponents. The trajectories of the sinusoidal-tent map can occupy all the phase planes over (0,4), while those of the two classic maps only occupy a small phase space, and the Lyapunov exponents of the ST map are all positive within the range of control parameters, higher than those of seed maps. Simultaneously, a novel quantum scrambling operation is devised based on the sinusoidal-tent map to avoid the periodicity of the quantum Arnold scrambling method. Initially, two chaotic sequences are generated to scramble the pixel positions of the watermark image, further enhancing the security of the watermarking algorithm. Subsequently, the host image is processed by the quantum discrete cosine transform, and finally, the scrambled watermark image is inserted into the medium-frequency band of the transformed host image, ensuring the invisibility of the watermarking. According to the simulation results, the quantum watermarking algorithm has excellent invisibility and robustness. [ABSTRACT FROM AUTHOR]

Secure transmission of images over wireless communications systems can be done using RSA, the most known and efficient cryptographic algorithm, and OFDMA, the most preferred signal processing choice in wireless communications. This paper aims to investigate the performance of OFDMA system for wireless transmission of RSA-based encrypted images. In fact, the performance of OFDMA systems; based on different signal processing techniques, such as, discrete sine transforms (DST) and discrete cosine transforms (DCT), as well as the conventional discrete Fourier transforms (DFT) are tested for wireless transmission of gray-scale images with/without RSA encryption. The progress of transmitting the image is carried by firstly, encrypting the image with RSA algorithm. Then, the encrypted image is modulated with DFT-based, DCT-based, and DST-based OFDMA systems. After that, the modulated images are transmitted over a wireless multipath fading channel. The reverse operations will be carried at the receiver, in addition to the frequency domain equalization to overcome the channel effect. Exhaustive numbers of scenarios are performed for study and investigation of the performance of the different OFDMA systems in terms of PSNR and MSE, with different subcarriers mapping and modulation techniques, is done. Results indicate that the ability of different OFDMA systems for wireless secure transmission of images. However, the DCT-OFDMA system showed superiority over the DST-OFDMA and the conventional DFT-OFDMA systems. [ABSTRACT FROM AUTHOR]

This research paper aims to forecast equipment's remaining useful life (RUL) to improve maintenance planning and reduce costs. This paper presents the spectral-based transformer (SPT) model, designed for predicting the remaining useful life (RUL) in the evolving maintenance landscape of industry 4.0. Proactive maintenance is becoming increasingly important as it improves performance and reduces losses. SPT utilizes advanced attention mechanisms and innovations, which have been evaluated on the C-MAPSS dataset to simulate various operations. The contributions include discrete cosine transform attention (DCTA), uninhibited positional and contextual attention (UPCA), multi-head shortcuts, and bidirectional structures. Component efficacy is rigorously assessed through ablations. The results demonstrate that SPT exhibits superior performance compared to other methods, with a notable advantage on the challenging FD002 and FD004 sub-datasets within the C-MAPSS dataset. The proposed method decreases the root mean square error (RMSE) by 14% and enhances the performance scores of FD002 and FD004 by 10% and 24%, respectively. Additionally, it reduces the RMSE of FD004 by 15%. The model outperforms current methods, showing stability and generalization across different subsets of data. SPT demonstrates proficiency in capturing degradation patterns, which shows the potential for accurate remaining useful life (RUL) prediction. This tool is designed for time-series regression and has potential applications in various industries. Future research could focus on expanding the system's capabilities to process higher frequencies and broader contexts effectively. The SPT method provides a thorough approach for predicting the remaining useful life (RUL). It can potentially improve maintenance decisions and system performance in the context of Industry 4.0. [ABSTRACT FROM AUTHOR]

Currently, security enhancement of biometric systems is an important issue that deserves consideration. This is attributed to the threats facing traditional recognition systems, which depend on Personal Identification Numbers (PINs) that can be stolen, easily. Utilization of original biometrics to access user services may lead to loss of the biometrics forever, if hacking attempts succeed in gaining access to the storage database of original templates. To address this concern and to avoid the utilization of original biometrics, we keep them away from being compromised through the utilization of cancelable biometric templates. This paper introduces a novel methodology for user authentication with multiple biometrics to generate distorted non-invertible cancelable templates to be stored in the database. The proposed framework begins with Discrete Cosine Transform (DCT) to achieve data compression in a multi-biometric scenario. After that, Double Random Phase Encoding (DRPE) is applied to increase the security level of the generated templates. Finally, an adaptive filter is used to induce an effect of whitening to generate the cancelable biometric templates. The generated patterns are uncorrelated due to the effect of encryption and adaptive filtering, which improves the security level against identity theft and provides good performance. Simulation results prove a good performance of the proposed cancelable biometric recognition framework with an Area under the Receiver Operating Characteristic curve (AROC) of 52.12% and an Equal Error Rate (EER) of 44.8462%. [ABSTRACT FROM AUTHOR]

Several medical consultations and examinations have been undertaken online since the Covid outbreak. However, when private data was communicated over the internet or uploaded to the cloud, medical information became more susceptible to security risks. Steganography is a technique that can be used to hide sensitive information within a cover image. This paper presents four improved algorithms to enhance steganography's performance in medical images. A full-size hidden image that is as huge as a cover image cannot be handled by previous methods, which is what the algorithmic design is meant to address. Several creative methods are presented, including the computation of Discrete Cosine Transform (DCT) coefficients based on scaled floating values, the addition of an adaptive compression matrix, and a new approach for systematically dispersing a concealed number of bits across multiple separate locations in the cover image. The results of the experiment showed a notable advancement over the earlier research. Our secret image size is substantially larger than the past studies, yet the structure similarity index matrix (SSIM) of the best reconstructed secret image is close to ideal, the peak signal-to-noise ratio (PSNR) and the payload capacity are higher than in the previous studies. This research is beneficial since it contributes to a medical application for enhancing the security of information concealed in chest X-ray images. Medical personnel can generate an image that conceals patient information in a secure manner. [ABSTRACT FROM AUTHOR]

This paper studied the problem of multi-user blind signal separation (BSS) in wireless communications. The existing separation algorithms work on quadrature phase shift keying (QPSK). Through our work, two proposed algorithms were presented to enhance the BSS performance. The first proposed algorithm uses wavelet denoising to remove noise from the received signals in time domain. It adopts different modulation techniques such as minimum shift keying (MSK), QPSK, and Gaussian minimum shift keying (GMSK). Then several BSS algorithms such as independent component analysis (ICA), principle component analysis (PCA), and multi-user kurtosis (MUK) algorithms were implemented. The second proposed algorithm transferred the problem of BSS to transform domain and used wavelet denoising to reduce noise effect on received mixture. The BSS with Discrete Sine Transform (DST) and Discrete Cosine Transform (DCT) were investigated and compared to time domain performance. Minimum square error (MSE) and signal to noise ratio (SNR) were used as the evaluating metrics for comparison. Simulation results proved that in time domain, MUK with QPSK gave best performance and wavelet denoising was found to enhance the performance of BSS under all conditions. Signal separation in transform domain was found to give better performance than that in time domain due to the energy compaction process of these transforms and noise reduction due to their averaging effect. [ABSTRACT FROM AUTHOR]

Developing efficient and secure image encryption techniques for transmitting multiple images has become crucial due to the inadequacy of single-image encryption techniques in handling the increasing volume of big data over unprotected networks. This paper introduces a novel multiple-image encryption (MIE) technique that utilizes mixed image elements in conjunction with the RSA cryptosystem, fractional discrete cosine transform (FrDCT), and Henon chaotic map (HCM). To encrypt k images together, the first step involves making three big images B 1 , B 2 and B 3 from these k images using matrix theory. The three images B 1 , B 2 and B 3 are converted into three indexed images I 1 , I 2 , and I 3 by extracting their color maps. Indexed images I 1 , I 2 and I 3 are then treated as a single RGB image by taking I 1 as a red (R) component, I 2 as a green (G) component and I 3 as a blue (B) component. The RSA cryptosystem is then applied on each component individually, followed by FrDCT and HCM to enhance security and key space. The resulting encrypted image is a single-channel real-valued image that is easy to display, store, and transmit over an unsecured network. The suggested technique offers multi-layer security in frequency, time and coordinate domains. Private keys, their arrangements and parameter positions are critical for decryption. Simulation analysis supports the robustness and effectiveness of the proposed technique. The sensitivity analysis demonstrates its extreme sensitivity towards private keys and their arrangement. Statistical analysis, including measures such as MSE, SSIM, PSNR, NPCR, UACI, entropy analysis, correlation coefficient, histogram analysis, computational complexity and comparison analysis confirm the effectiveness and viability of the introduced algorithm. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel watermarking method in order to enhance the security in biometric voice/speech transmission systems basing on sub-sampling, discrete cosine transform (DCT) and adaptive watermark embedding. To improve imperceptibility, we use sub-sampling and adaptive embedding in DCT high energy coefficients. We employ a significant watermark represented in a biometric unique fingerprint. Introducing bits, only in high energy fraction, provides us with further agreeing element depicted by decreasing running time, both in embedding and extraction processes, which can help minimizing hardware consumption. Achieved results reveal the stability and flexibility of our proposed scheme and confirm its robustness against additive noise. In addition, we enhanced our previous published approaches and the expected limitations of our proposed model will appear in the hardware implementation where the attacks will be more considered. Another limitation will be the number of quantization bits used for high energy DCT coefficients. All these may affect the speaker secured identification and verification system. [ABSTRACT FROM AUTHOR]

This research paper presents a modified blind and robust image watermarking scheme that combines dual-tree complex wavelet transform (DTCWT) and discrete cosine transform (DCT) domains. A key challenge for researchers is to determine the optimal locations for embedding watermarks in the low-frequency coefficients of the hybrid domains, ensuring both imperceptibility and security. To identify the most effective sequence for the watermark embedding process, a differential approach is implemented on two correlated DCT-transformed vectors derived from DTCWT wavelet low-frequency coefficients. The watermark data does not need to be extracted from the original image. The proposed scheme aims to assess the efficiency improvement against various image processing attacks. We utilized fifteen grayscale images from the UCI-sipi image database, each with a size of 512 × 512 pixels, to evaluate the proposed scheme. The experimental results demonstrate that our scheme outperforms existing schemes in common image attacks such as geometric attacks, compression, filtering, and noise addition. [ABSTRACT FROM AUTHOR]

With the advancements in multimedia communications, it has become apparent that there is a bad need to perform image communication with a confidence guarantee. We need to have a guarantee of image integrity at the receiver. Towards this objective, a proposed framework is presented in this paper. This framework comprises self-signature embedding in the transmitted images. The signatures are extracted from image blocks in the Discrete Cosine Transform (DCT) domain and embedded in other blocks in the same domain with a certain weight to avoid deteriorating the resulting image quality. A verification process is performed at the receiver to check whether the content has been modified or not. In addition, image watermarking is also used with DCT and Discrete Wavelet Transform (DWT) algorithms for authentication or verification. Moreover, Double Random Phase Encoding (DRPE) algorithm is used to secure the content of transmitted images. Then, to get a higher level of security for transmitted images, a hybrid technique depending on DCT-based signature embedding and the DRPE algorithm is presented. [ABSTRACT FROM AUTHOR]

Imaging of the first metres of the subsurface with seismic methods constitutes a key challenge for several applications. In this context, the analysis of Rayleigh waves can reveal information about the S-wave velocity structure in the first metres of the subsurface. The waves recorded can be inverted using several techniques, of which the most widely used is the multichannel analysis of surface waves, where dispersion curves are picked on the velocity-frequency spectrum. A full-waveform inversion of surface waves has been implemented, offering the possibility to exploit the complete information content of the recorded seismograms. This method has only recently been tested with elastic approximation on synthetic data, as the application in near-surface scenarios is very challenging due to the high nonlinearity of the problem and the considerable computational costs. This paper presents a gradient-based Markov chain Monte Carlo elastic full-waveform inversion method, where posterior sampling is accelerated by compressing data and model spaces through the discrete cosine transform and, also, by defining a proposal that is a local, Gaussian approximation of the target posterior probability density. The applicability of the approach is demonstrated by performing two synthetic inversion tests on two different near-surface models: a two-layered model with lateral velocity variations, and a four-layered model with velocity inversions. [ABSTRACT FROM AUTHOR]

In this modern era, it has become much simpler to replicate, sell, and copy the copyright owners' works without their permission as a result of the expansion of digitalization, and it is difficult to identify such violations, posing a threat to the creators' and copyright owners' rights. For many years, the internet has been regarded as one of the most serious threats to copyright, and the content available has varying levels of copyright protection. On the internet, there are numerous copyrighted works, including e-books, movies, news, and so on. Therefore, by using watermarking and steganography techniques, these issues can be solved, which are based on the author's signature information or logo. This paper concluded that the techniques of discrete cosine transform (DCT), discrete wavelet transform (DWT), one-time pad (OTP), and playfair are highly effective when used together to watermark an image or embed a secret message, our lab results validate that our algorithm scheme is robust against several sets of attacks, where the algorithm was assessed by computation of many evaluation metrics such as mean square error (MSE), signal-to-noise ratio (SNR), and peak signal-to-noise ratio (PSNR). [ABSTRACT FROM AUTHOR]

In today's communication era, social media and sharing data through the world wide web plays a substantial role. As per a recent survey, 80% of communication these days is carried out through the Internet. Moreover, the explosive growth of technology has transformed mobile devices into indispensable tools in the computing world. In short, the significant progress of technology has made accessing and modifying data quick and more straightforward. However, the ease of usage of multimedia over the net has opened a door for many attacks and piracy acts, where security and authentication play a vital role. This paper presents a novel video watermarking approach using hybrid discrete cosine transforms for copyright protection and authentication. The main objective of this method is to develop robust and efficient watermarking techniques for videos on mobile devices. Mobile devices function within the limitation of restricted storage and battery life. Therefore, there is heightened emphasis on the analysis of power consumption and execution time during the design of mobile applications. Our preliminary work draws a clear conclusion on the efficient video watermarking approach required for mobile devices concerning power consumption and execution time. In addition, we have illustrated a comparative analysis of existing works on multimedia security and authentication with the proposed watermarking technique on video. [ABSTRACT FROM AUTHOR]

In this new age, because of the exceptional achievement of the global computer network, the trading of digital media over the web has turned out to be unbelievably easy. However, securing the exclusive rights of the owner while trading digital media is an important issue. Digital image watermarking is a method to ensure copyright protection, security, and authenticity of data. In this paper, a novel technique is presented which is optimized, secure, and robust. The Watermark is set solidly in the discrete cosine transform domain. It is also encrypted based on the proposed block shuffling algorithm to increase security. The method was examined against various attacks, and it succeeded in maintaining statistical significance in terms of robustness and imperceptibility, as the average Peak Signal to Noise Ratio value is 65 dB, and the average Normalized Correlation value is close to one after apply all possible attacks. The results have been tested on MATLAB 2020a. [ABSTRACT FROM AUTHOR]

The extensive spread of fake news on social networks is carried out by a diverse range of users, encompassing private individuals, newspapers, and organizations. With widely accessible image and video editing tools, malicious users can easily create manipulated media. They can then distribute this content through multiple fake profiles, aiming to maximize its social impact. To tackle this problem effectively, it is crucial to possess the ability to analyze shared media to identify the originators of fake news. To this end, multimedia forensics research has advanced tools that examine traces in media, revealing valuable insights into its origins. While combining these tools has proven to be highly efficient in creating profiles of image and video creators, it is important to note that most of these tools are not specifically designed to function effectively in the complex environment of content exchange on social networks. In this paper, we introduce the problem of establishing associations between images and their source profiles as a means to tackle the spread of disinformation on social platforms. To this end, we assembled SocialNews , an extensive image dataset comprising more than 12,000 images sourced from 21 user profiles across Facebook, Instagram, and Twitter, and we propose three increasingly realistic and challenging experimental scenarios. We present two simple yet effective techniques as benchmarks, one based on statistical analysis of Discrete Cosine Transform (DCT) coefficients and one employing a neural network model based on ResNet, and we compare their performance against the state of the art. Experimental results show that the proposed approaches exhibit superior performance in accurately classifying the originating user profiles. • We introduce SocialNews, a novel dataset for disinformation detection online. • Images were sourced primarily from social profiles of news agencies around the world. • The goal is to automatically identify the profile of the user that shared an image. • We introduce two benchmarks methods: a DCT-based one and a ResNet-based one. • Experiments show that the proposed benchmark methods outperform the state of the art. [ABSTRACT FROM AUTHOR]

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This paper proposes a novel medical image watermarking scheme for a multiuser wireless system based on data normalization and polar code. The watermark image is normalized, converted into a set of coefficients using Discrete Cosine Transform (DCT), and then embedded with host medical image coefficients. The watermark image is encrypted using a Baker map and then normalized. The normalized value is converted to binary data, then XORed with a logistic map sequence, and employed as frozen bits in polar code to achieve security, robustness, and imperceptibility over the Non-Orthogonal Multiple Access-Universal Filtered Multicarrier (NOMA-UFMC) transmission scheme. A collection of medical images was employed to evaluate the proposed technique for two users, and the findings are Peak Signal to Noise Ratio (PSNR=69.5761), structural similarity index (SSIM=0.9966), mean square error (MSE=0.4399) and Normalized Cross-Correlation (NCC=0.9997) which demonstrate that it is capable of achieving a high level of imperceptibility and robustness against attacks. In addition, the proposed scheme produced good Bit Error Rate (BER) performance by exploiting the properties of polar code. Compared with the traditional techniques, the proposed approach has higher image quality, a secure watermark image, performs high robustness against various attacks, and has low execution time (about 0.3284 seconds). The suggested method is, therefore, a promising one for protecting medical images in healthcare settings. [ABSTRACT FROM AUTHOR]

With current advancement in hybermedia knowledges, the privacy of digital information has developed a critical problem. To overawed the susceptibilities of present security protocols, scholars tend to focus mainly on efforts on alternation of current protocols. Over past decade, various proposed encoding models have been shown insecurity, leading to main threats against significant data. Utilizing the suitable encryption model is very vital means of guard against various such, but algorithm is selected based on the dependency of data which need to be secured. Moreover, testing potentiality of the security assessment one by one to identify the best choice can take a vital time for processing. For faster and precisive identification of assessment algorithm, we suggest a security phase exposure model for cipher encryption technique by invoking Support Vector Machine (SVM). In this work, we form a dataset using usual security components like contrast, homogeneity. To overcome the uncertainty in analysing the security and lack of ability of processing data to a risk assessment mechanism. To overcome with such complications, this paper proposes an assessment model for security issues using fuzzy evidential reasoning (ER) approaches. Significantly, the model can be utilised to process and assemble risk assessment data on various aspects in systematic ways. To estimate the performance of our framework, we have various analyses like, recall, F1 score and accuracy. [ABSTRACT FROM AUTHOR]

Images can store large amounts of data and are useful for transmitting large amounts of information across different geographical locations using different cloud services. This data sharing increases the chances of cyber-attacks on digital images. Blockchain has properties that enable it to work as a solution to this problem, providing enhanced security and unchangeable storage. However, image size poses a challenge in image storage, as it increases the related storage cost. Compressing images using fractional discrete cosine transform (fctDCT) reduces the amount of data required to express an image securely. This paper presents a novel framework for securely storing and retrieving medical images by extracting feature maps from medical images using fctDCT, followed by encoding and storing a feature map on a decentralized cloud and linking it on a blockchain. The integration has been implemented using α angles, which are stored on the blockchain and need to be identical at the storage and retrieval stage, as only the authentic user would have access to unique α angles and the number of coefficients that have been used in storing their medical images. The proposed novel approach offers numerous benefits, including improved data sharing and collaboration, enhanced security, compression, and efficient retrieval and processing of medical image data. The performance of the proposed framework was evaluated in terms of image quality metrics such as mean square error, peak signal-to-noise ratio, structural similarity index measure (SSIM), and multiSSIM by employing it with correct and incorrect α values. [ABSTRACT FROM AUTHOR]

Image compression is an important stage in picture processing since it reduces the data extent and promptness of image diffusion and storage, whereas image reconstruction helps to recover the original information that was communicated. Wavelets are commonly cited as a novel technique for image compression, although the production of waves proceeding smooth areas with the image remains unsatisfactory. Stockwell transformations have been recently entered the arena for image compression and reconstruction operations. As a result, a new technique for image compression based on the improved Stockwell transform is proposed. The discrete cosine transforms, which involves bandwidth partitioning is also investigated in this work to verify its experimental results. Wavelet-based techniques such as multilevel Haar wavelet, generic multiwavelet transform, Shearlet transform, and Stockwell transforms were examined in this paper. The MATLAB technical computing language is utilized in this work to implement the existing approaches as well as the suggested improved Stockwell transform. The standard images mostly used in digital image processing applications, such as Lena, Cameraman and Barbara are investigated in this work. To evaluate the approaches, quality constraints such as mean square error (MSE), normalized cross-correlation (NCC), structural content (SC), peak noise ratio, average difference (AD), normalized absolute error (NAE) and maximum difference are computed and provided in tabular and graphical representations. [ABSTRACT FROM AUTHOR]

The possibilities offered by the use of variational mode decomposition–based Hilbert marginal spectrum and the differential cepstrum for gas‐bearing detection are studied in this paper. We propose a novel variational mode decomposition–based Hilbert marginal differential cepstrum for hydrocarbon detection. Variational mode decomposition–based Hilbert marginal spectrum is first computed. Then discrete cosine transform is carried out to the differential logarithmic variational mode decomposition–based Hilbert marginal spectrum to obtain the variational mode decomposition–based Hilbert marginal differential cepstrum. For hydrocarbon detection, the seismic amplitude anomaly section is generated by extracting the first and second common quefrency sections. Compared with the traditional Fourier‐based cepstrum, the wavelet‐based cepstrum and the Berthil cepstrum, it has the ability to effectively reveal more detailed frequency‐dependent amplitude anomalies with high accuracy and resolution. Model tests and field data applications from a carbonate reservoir in China show that the variational mode decomposition‐based Hilbert marginal differential cepstrum can provide a better gas‐prone interpretation. The proposed method can be a complementary approach to current cepstrum‐based hydrocarbon detection methods and the spectrum decomposition methods. [ABSTRACT FROM AUTHOR]