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This study proposes an intelligent impulse denoising technique that utilizes sequential region expansion to address the limitations of traditional denoising methods such as mean and median filters. The proposed algorithm selectively identifies noisy pixels and adaptively expands the filtering region based on local noise density. This adaptive approach minimizes the processing of non-noisy pixels, thereby preserving important image details and maintaining visual fidelity. Extensive experiments on grayscale and color images validate the effectiveness of the proposed method. The algorithm shows improved performance in noise reduction and detail preservation compared to conventional techniques, including adaptive and weighted median filters. Quantitative evaluations using Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM) metrics demonstrate reliable performance across varying noise intensities, maintaining high image quality. [ABSTRACT FROM AUTHOR]

Glaucoma is a progressive eye disease. This study presents a custom technique to enhance retinal fundus images to detect glaucoma. Contrast enhancement is a crucial stage in medical image analysis to improve the visual impression of diseases. CLAHE is a common technique to improve images. Clip Limit (CL) and subimages may restrict the potential benefits of the typical approach and pose difficulties. This study introduces Enhanced CLAHE and Automated CLAHE to address the shortcomings of the base method. These methods demonstrate progress in improving retinal landmarks in various ways by looking directly at the in-depth description of retinal images. The proposed methods, along with the baseline CLAHE, were compared using quality assessment tools such as the Peak-Signal-to-Noise Ratio (PSNR). The results help to determine the degree of contrast enhancement and the overall richness of the image. [ABSTRACT FROM AUTHOR]

The demand for real-time 4K video streaming has introduced technical challenges due to the high bandwidth, low latency, and minimal jitter required for high-quality user experience. Traditional single-path networks often fail to meet these requirements, especially under network congestion and packet loss conditions, which degrade video quality and disrupt streaming stability. This study evaluates Multipath tunnel- Generic Routing Encapsulation (MPT-GRE), a technology designed to address these challenges by enabling simultaneous data transmission across multiple network paths. By aggregating bandwidth and adapting dynamically to network conditions, MPT-GRE enhances resilience, maintains quality during network disruptions, and offers throughput nearly equal to the sum of its physical paths' throughput. This feature ensures that even if one path fails, the technology seamlessly continues streaming through the remaining path, significantly reducing interruptions. We measured key video quality metrics to assess MPT-GRE's performance: Structural Similarity Index Measure (SSIM), Mean Squared Error (MSE), and Peak Signal-to-Noise Ratio (PSNR). Our results confirm that the MPT-GRE tunnel effectively improves SSIM, PSNR, and reduces MSE compared to single-path streaming, offering a more stable, high-quality viewing experience. Our results indicate that analyzing the SSIM, MSE, and PSNR values for 4K video streaming using the MPT tunnel demonstrates a significant performance improvement compared to a single path. The improvement percentages of the SSIM and PSNR values for the MPT tunnel are (29.05% and 29.04%) higher than that of the single path, while MSE is reduced by 81.17% compared to the single path. [ABSTRACT FROM AUTHOR]

Telemedicine and teleconsultations have become rampant today, consequent to the pandemic scenario as well as the availability of seamless internet connectivity. In this context, maintaining the integrity and confidentiality of a medical record has become more significant than ever before. A novel data-hiding methodology based upon Grain 128a algorithm and two-dimensional logistic mapping for the security of biomedical images in applications of telemedicine is proposed in this research article. In this endeavor, the name of the medical practitioner and diagnosis results are embedded into the MRI brain image of a patient with astrocytoma. A highlight of this work is the use of a multilevel system for enhanced security. Initially, Grain 128a is used for deriving the initial factors of the logistic mapping using a 512-bit confidential key. Grain 128a is able to withstand various types of single-key attacks. Logistic Mapping is used to generate two sequences, one for the X and the other for the Y direction. Grain 128a algorithm and logistic mapping are combined to enhance the security as well as the sensitivity of the data hiding system for a single variation in the secret key. A bit diffusion and confusion process is used to identify the pixel locations in which the name of the practitioner and the diagnosis result must be embedded. The doctor's name and diagnosis result are converted into binary and introduced into the least significant bit positions (LSBs) of those pixel locations. Finally, encryption is done on the data-hided image using Elliptical Curve Cryptography (ECC) for enhanced security. The system performance is analyzed using different measures such as histogram analysis, structural similarity index (SSIM), universal image quality index (UIQI), Pearson's correlation coefficient (PCC), entropy, peak signal-to-noise ratio (PSNR), etc. The novel system is compared with two existing techniques and observed that it has better performance in terms of PSNR and SSIM. The security of the encryption system is measured by means of histogram analysis, correlation coefficient, PSNR, entropy, etc. The performance of the encryption system has been compared with the current state of art technique and it is found that the proposed system outperforms the current state of art techniques in every performance measure. [ABSTRACT FROM AUTHOR]

Introduction: Wavelet thresholding techniques are crucial in mitigating noise in data communication and storage systems. In image processing, particularly in medical imaging like MRI, noise reduction is vital for improving visual quality and accurate analysis. While existing methods offer noise reduction, they often suffer from limitations like edge and texture loss, poor smoothness, and the need for manual parameter tuning. Methods: This study introduces a novel adaptive wavelet thresholding technique for noise reduction in brain MRI. The proposed method utilizes a linear prediction factor to adjust the threshold adaptively. This factor leverages temporal information and features from both the original and noisy images to determine a weighted threshold. This dynamic thresholding approach aims to selectively reduce or eliminate noise coefficients while preserving essential image features. Results: The proposed method was rigorously evaluated against existing state-of-the-art noise reduction techniques. Experimental results demonstrate significant improvements in key performance metrics, including mean squared error (MSE), peak signal-to-noise ratio (PSNR), and structural similarity index (SSIM). Discussion: The proposed adaptive thresholding technique effectively addresses the limitations of existing methods by providing a more efficient and accurate noise reduction approach. By dynamically adjusting the threshold based on image-specific characteristics, this method effectively preserves image details while effectively suppressing noise. These findings highlight the potential of the proposed method for enhancing the quality and interpretability of brain MRI images. [ABSTRACT FROM AUTHOR]

Existing night‐time dehazing methods had been attempting to process light and non‐light source regions based on dark channel prior (DCP). Since the bright and non‐bright regions exhibit different features, the same daytime method cannot be applied to night images because light scatter from the multiple objects non‐uniformly and DCP tends to over‐estimate the depth of the scene making the image unrealistic. To overcome this limitation, wavelet decomposition was performed so that haze remains in the low occurrence region and noise in the high occurrence region and noise was removed by soft thresholding method. In the presented approach, the open DCP (ODCP) transmission map was computed for handling light source regions and estimated transmission was refined to enhance the texture in high‐frequency part. Bilinear interpolation method of fast‐guided filtering and recursive filter in the domain transform was used for edge preservation, enhancement of texture details and smoothness. The dehazed image was constructed by correlating the coefficients of low occurrence part recovered from haze and high occurrence component. The performance analysis was compared against state‐of‐the‐art methods in terms of peak signal‐to‐noise ratio (PSNR) and Structural Similarity Index (SSIM). [ABSTRACT FROM AUTHOR]

Steganography aims to conceal secret data within images without affecting image quality. Traditional methods often struggle with balancing simplicity, effectiveness and payload capacity while maintaining imperceptibility. Proposed algorithm: the paper proposed a novel steganographic mshEdgeRGB_T1 algorithm that combines Mamdani fuzzy type-1 logic with the least significant bit (LSB) method. The LSB method is chosen for its simplicity and effectiveness in hiding messages. The mshEdgeRGB_T1 algorithm focuses on embedding secret messages in edge pixels, detecting more edge pixels compared to other methods, thus increasing payload capacity. Findings: the algorithm's performance is evaluated using metrics such as peak signal-to-noise ratio (PSNR), mean squared error (MSE) and histogram analysis to measure the similarity between the cover and Stego images, quantifying the level of imperceptibility. Experimental analysis demonstrates that the mshEdgeRGB_T1 algorithm offers improved payload capacity, enhanced security and reduced imperceptibility compared to many existing methods. Conclusion: the proposed mshEdgeRGB_T1 algorithm effectively balances simplicity, payload capacity and image quality, making it a better use for image steganography. [ABSTRACT FROM AUTHOR]

Quantum image computing has attracted attention due to its vast storage capacity and faster image data processing, leveraging unique properties such as parallelism, superposition, and entanglement, surpassing classical computers. Although classical computing power has grown substantially over the last decade, its rate of improvement has slowed, struggling to meet the demands of massive datasets. Several approaches have emerged for encoding and compressing classical images on quantum processors. However, a significant limitation is the complexity of preparing the quantum state, which translates pixel coordinates into corresponding quantum circuits. Current approaches for representing large-scale images require higher quantum resources, such as qubits and connection gates, presenting significant hurdles. This article aims to overview the pixel intensity and state preparation circuits requiring fewer quantum resources and explore effective compression techniques for medium and high-resolution images. It also conducts a comprehensive study of quantum image representation and compression techniques, categorizing methods by grayscale and color image types and evaluating their strengths and weaknesses. Moreover, the efficacy of each model's compression can guide future research toward efficient circuit designs for medium- to high-resolution images. Furthermore, it is a valuable reference for advancing quantum image processing research by providing a systematic framework for evaluating quantum image compression and representation algorithms. [ABSTRACT FROM AUTHOR]

With the advancement in information technology confidential data security and privacy in data transmission have long been a top priority because of the increase in cybercrime. A number of cryptographic and steganography methods were put up to resolve the issues. Reversible data hiding, due to its versatility and ability to be used in a variety of settings, is a great strategy for protecting sensitive information. However, it can handle only little payloads with good quality image carrying data.This paper introduces a novel spatial image steganography scheme that combines Differential Expansion (DE) and RC4 cryptography to significantly improve data security. The methodology involves embedding sensitive information into the difference values of pixel pairs using DE, followed by RC4 encryption to ensure robust data protection. Our approach leverages the strengths of DE for high-capacity data embedding and the robust encryption capabilities of RC4 to protect against unauthorized access and tampering.To evaluate the effectiveness of our proposed method, we conducted extensive experiments using a variety of image datasets. The results demonstrate that our method achieves significant improvements in payload capacity, security, and image quality compared to traditional techniques. Specifically, our method outperforms existing methods such as LSB, Hamming code, and GAN-based approaches in key metrics including Peak Signal-to-Noise Ratio (PSNR), Mean Square Error (MSE), and Structural Similarity Index Metric (SSIM). These metrics indicate that our method maintains high image quality while providing enhanced data security.

This research proposes a text data security method using a combination of Linear Congruential Generator (LCG), Advanced Encryption Standard (AES) Cipher Block Chaining (CBC) mode, and Least Significant Bit (LSB) steganography technique on digital images. The message scrambling process using LCG produces ASCII characters as noise that is inserted in the original message. After that, the message is encrypted using AES-256 CBC to provide additional security. The encryption result is then hidden in the digital image through LSB steganography technique. Tests were conducted on images with JPEG and BMP formats to measure the visual quality after the data insertion process, as measured by PSNR (Peak Signal-to-Noise Ratio). The test results show a PSNR value of 56.60 dB for JPEG images and 70.84 dB for BMP images. In addition, the insertion process in JPEG images degrades the image quality, mainly due to lossy compression, compared to the lossless BMP format. This study concludes that the proposed combination of methods is effective in hiding messages in images, but is susceptible to compression on lossy formats such as JPEG. The use of lossless image formats such as BMP or PNG is recommended to maintain data integrity.

Underwater image enhancement and processing have gained prominence in the field of image processing due to marine scientists' interest in uncovering new species and environments. This work presented a lightweight, attention-based, deep hybrid convolutional neural network (LW-AB-DHCNN) architecture to enhance overall efficiency. The traditional CNNs use subnetworks to enhance model depth and achieve the same functionality, but the proposed method employs multiple depth-wise separable convolutions, thereby reducing the computational complexity of the system. The proposed approach integrates Deep CNN with CBAM to provide an enhanced U-Net model. CBAM employs a self-attention method to acquire both local and global data in underwater images, thereby augmenting their semantic interpretation. This work also employed a unified error function to direct the training and optimization of the model. When the presented scheme was evaluated against benchmark datasets, it achieved an average PSNR of 25.69 dB and an average SSIM of 0.8624. [ABSTRACT FROM AUTHOR]

Copyright of Baghdad Science Journal is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) 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.)

In order to address the issues of image quality degradation and distortion that arise in the context of video transmission coding and decoding, a method based on an enhanced version of CycleGAN is put forth. The lightweight attention module is integrated into the residual block of the generator module structure, thereby facilitating the extraction of image details and motion compensation. Furthermore, the perceptual function LPIPS loss is increased to align the image restoration effect more closely with human perception. Additionally, the network training method is modified, and the original image is divided into 128 × 128 small blocks for training, thus enhancing the network's accuracy in restoring details. The experimental results demonstrate that the algorithm attains an average PSNR value of 30.1147 on the publicly accessible YUV sequence dataset, YUV Trace Dataset, which is a 9.02% enhancement compared to the original network. Additionally, the LPIPS value reaches 0.2639, representing a 10.42% reduction, and effectively addresses the issue of image quality deterioration during transmission. [ABSTRACT FROM AUTHOR]

Hyperspectral remote sensing images obtained from cameras are characterized by high-dimensions and low quality, which makes them unfavorable for various analytics purposes. This is due to the presence of visible and invisible frequencies of the reflected light making it poorly reveal the spectral signatures of the image. Visual communication advancement has paved the need for Image Super-Resolution (SR) which recovers high-resolution images from low-resolution images. Several works were carried out earlier on image SR using variants of supervised and unsupervised models that still lack accuracy. In this paper, we propose an unsupervised learning model titled Gompertz Function–based Convergence War Accelerometric Optimization–GAN framework for generating of High-Resolution (HR) images. The framework comprises a pre-processing stage, where the incoming Low-Resolution (LR) image is preprocessed for noise removal by applying Shannon-Gaussian Filter (S-GF). Following is the Gradient Domain Approach based Tone-Mapping (TM). Skew correction is done to remove distortion and maintain original resolution that may change during TM stage. The next stage comprises the boundary and edge enhancement of the resulting preprocessed image generated by the method of Inverse Gradient Mapping (IGM) followed by patch extraction to extract minute low-frequency information from the resulting boundary and edge-enhanced image. The contrast of the enhanced patches is improved by removing blurriness effect. The preprocessed image patches are then fed into the Gompertz Function-based Convergence War Accelerometric Optimization – GAN for feature mapping on the trained SR Image features that are clustered using Krzanowski and Li- Kantorovich Metric-K-Means clustering Algorithm (KL-KM-KMA) for effective generation of SR image. The developed model is validated for both qualitative and quantitative measurements. Comparisons are made with several other state-of -the-art methods for accuracy of 98.05%, precision of 97.98%, inception score of 8.71, Fréchet Inception Distance of 36.4 with reduced clustering and training time proving the efficiency of the proposed model. [ABSTRACT FROM AUTHOR]

Early detection of congenital heart disease (CHD), one of the most commonly occurring congenital defects, is important to reduce mortality rates. The major drawback of ultrasound imaging is the inherent speckle noise, making visual examination of anatomical structures a challenging task. This study discusses the effect of denoising using different linear and nonlinear filters on B mode foetal cardiac ultrasound images. The exhaustive study of the performance of linear filters such as mean, Laplacian, and Wiener, and nonlinear filters such as median, anisotropic diffusion, non-local means, and bilateral filters has been carried out. Performance of the filtering technique is evaluated using various fidelity measures and a new assessment parameter edge preserving index (EPI) is also used for evaluation. Non-local means filter outperforms other filtering techniques with average fidelity values as follows: PSNR: 67.97 dB, MSE: 0.01283, RMSE: 0.109, average difference (AD): 0.005, normalized absolute error (NAE): 2 × 10−4, mean absolute error (MAE): 0.011, EPI: 0.999. This work serves as a reference on pre-processing techniques that can be applied to foetal cardiac ultrasound images for beginners. [ABSTRACT FROM AUTHOR]

In this paper, a watermarking technique based on discrete curvelet transform and discrete cosine trans- form is proposed to protect the color document images. The six layers of the document image are created using the Discrete Curvelet Transform (DCuT). The sixth layer is chosen for the embedding process, while the remaining layers are discarded. Discrete Cosine Transform (DCT) is applied on the 8 × 8 blocks of the real part of sixth layer. DCT coefficients from low to medium are selected for the embedding process, excluding the first one. To choose the Direct Current (DC) coefficients of DCT to embed the watermark bits, a zigzag function is used. The same process is applied on the receiver side watermark image is extracted. Since this is a non-blind method, we require both a cover image and a watermarked image. DCT of watermarked image and the cover image are compared to extract the watermark image. The resistivity of the watermarked image against image processing attacks is measured using the Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM), Mean Square Error (MSE), Normalization Correlation (NC), Bit Error Rate (BER) and Universal Quality Index (UQI). [ABSTRACT FROM AUTHOR]

With the development of new technologies, 3D models are becoming increasingly important. They are used to design new models, document cultural heritage and scan valuable artefacts or evidence. They are also used in medicine. For these reasons, they are vulnerable to forgery. Protection against forgery done by encrypting the model or signing it digitally may restrict access to the data or require additional files to store the signatures. A good way to confirm the originality of 3D models is fingerprinting. This technique involves attaching a fragile watermark directly to the watermarked data. In the paper, we propose a new fingerprinting method for 3D models. The method hides the fingerprint in the least significant digits of the coordinates of the selected vertices. The fingerprint is created by calculating the hash-based message authentication code (HMAC) from the model textures and all vertex coordinates except the digits intended to attach the fingerprint. These digits are processed using discrete wavelet transform (DWT). The HMAC is attached to the selected DWT coefficients. The inverse discrete wavelet transform is then performed to obtain the new values of the modified digits. The digits are put back into the 3D model coordinates and the model is reassembled. Verification of the model originality is done according to the used steganographic key and consists of comparing the HMAC value extracted from the fingerprinted model with the HMAC value calculated from it. The same values of both HMAC results indicate that the model has not been modified. The proposed method allows efficient model fingerprinting and detection of changes made to any part of the model. The included fingerprints are transparent - the peak signal-to-noise ratio (PSNR) of a fingerprinted model can reach 150dB and its structural similarity can be over 99.8%. This paper presents a novel, computationally efficient fragile watermarking scheme that is capable of detecting the smallest changes in any part of a 3D model. The presented solution can be used to confirm the originality of 3D models. In particular, it will work well for fingerprinting large models such as 3D scans of architectural objects. [ABSTRACT FROM AUTHOR]

Hyperdimensional computing (HDC) has emerged as a promising paradigm offering lightweight yet powerful computing capabilities with inherent learning characteristics. By leveraging binary hyperdimensional vectors, HDC facilitates efficient and robust data processing, surpassing traditional machine learning (ML) approaches in terms of both speed and resilience. This letter addresses key challenges in HDC systems, particularly the conversion of data into the hyperdimensional domain and the integration of HDC with conventional ML frameworks. We propose a novel solution, the hyperdimensional vector quantized variational auto encoder (HDVQ-VAE), which seamlessly merges binary encodings with codebook representations in ML systems. Our approach significantly reduces memory overhead while enhancing training by replacing traditional codebooks with binary (−1, +1) counterparts. Leveraging this architecture, we demonstrate improved encoding-decoding procedures, producing high-quality images within acceptable peak signal-to-noise ratio (PSNR) ranges. Our work advances HDC by considering efficient ML system deployment to embedded systems. [ABSTRACT FROM AUTHOR]

Video analytics can achieve increased speed and efficiency by operating directly on the compressed video format, thereby alleviating the decoding burden on the analytics server. The encoded video streams are rich in semantic binary information and this information can be utilized more efficiently to train the classifiers. Motivated by the same notion, a deep learning-based video compression-cum-classification network has been proposed. In the proposed work, the binary-coded semantic information is extracted by using an auto encoder-based video compression component and the same fed to the MobileNetv2-based classifier for the classification of the given video streams based on their content. Using large-scale user-generated content provided by YouTube UGC dataset, it has been demonstrated that using deep neural networks for compression not only provides on-par compression results to traditional methods, it makes analytical processing of these videos faster. Video content tagging of YouTube UGC dataset has been used as the analytics task. The proposed DLVCC approach performs 10 × faster with 30 × fewer parameters than MobileNetv2 in video tagging of compressed video with no loss in accuracy. [ABSTRACT FROM AUTHOR]

Transmission of images over a long distance is a need of the day for different applications. As a lot of information is carried by high-resolution images, huge bandwidth is required to transmit them. So, to make efficient transmissions, compression of images is very important. Again, images involved in various applications carry confidential information and need to be highly secured before transmission. For the purpose of secured transmission, images must be encrypted with robust encryption schemes. In this communication, an effective and secured method for frequency-domain compression and transmission of infrared images is developed which is equally applicable for any other images. It is shown in this communication that the compression ratio can be improved by the proposed technique without compromising the PSNR value. In this technique, the compression ratio is improved from 2.79 to 11.13 without degradation in PSNR value of retrieved images (around 34). Our compression technique involves the modulation of images using amplitude grating. Different orientation angles and grating frequencies are selected for modulation. Security of transmitted images is ensured by 6-D hyper-chaotic encryption. This method is efficient enough to encrypt, transmit, and recover multiple infrared images without the occurrence of aliasing errors. The algorithm shows satisfactory performance for applications where huge image data are to be transmitted in a highly secure way. [ABSTRACT FROM AUTHOR]

Copyright of Baghdad Science Journal is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) 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 research proposes a learning-based efficient single-image dehazing method. Dehazing, discriminator, and fine-tuning networks build the end-to-end network model. These three techniques are independently trained on suitable datasets. An end-to-end network architecture improves dehazing. The dehazing network model estimates transmission map, atmospheric light, and parallel convolution layers to analyze the input hazy image. The discrimination network extracted a discriminated dehazing image. Finally, discriminator network model findings are used for fine-tuning. The suggested model is tested using foggy images from various datasets and performance measures including PSNR, SSIM, MSE, and Entropy. The suggested learning-based image dehazing is compared to existing approaches qualitatively and quantitatively. The suggested approach improves PSNR by 34.3% to 3.65% over previous works. The proposed work has a 24.9% higher average SSIM and a 76% lower MSE than current efforts. The entropy of the proposed work is improved by a maximum of 9.38%. [ABSTRACT FROM AUTHOR]

Medical image processing generally contains high components of noise produced by interference, compression and use of imperfect instrument during acquisition or transmission. An effective imaging devicei.e. Magnetic Resonance Imagingmay diagnose the disease by acute analysis of dissectional anatomical soft tisses of human. In general, MR images are of poor contrast in lieu of blurriness, out of focused and lack of brightness iside the machine. In this paper, hybridization approaches i.e. Median-Wiener filter (MW), Wiener-Median filter (WM) and other combinations like WMWM & MWMW are proposed for MR image enhancement. The results are further compared with various filtering algorithms i.e. Average filter, Median Filter, Wiener Filter &Gaussian filter and in terms of MSE, PSNR, RMSE, MAE. The proposed hybridization filtering technique gives better outcomes comparatively. [ABSTRACT FROM AUTHOR]

Currently, evaluators assess the quality of content transmitted by computing devices, such as smartphones and computers, based on the success or failure of the human visual system. However, many failures go unnoticed because of the flow of frames and the large volume of information transmitted. This paper proposes automatic monitoring of the quality of multimedia content transmitted between a smartphone and a digital TV via a wireless transmission channel. The methodology combines two tools: the peak signal-to-noise ratio (PSNR) and the structural similarity index (SSIM). To receive the transmitted data, we establish intermediation through a hotspot and monitor it using the Wireshark tool. The results indicate a 99.42% accuracy rate for local media and 99.02% accuracy in processing media consumed from streaming channels. Based on test and analysis results, this research concludes that the proposed architecture allowed a better measure of imperceptibility in aspects where human vision is more sensitive, such as color changes. [ABSTRACT FROM AUTHOR]

Bird research contributes to understanding species diversity, ecosystem functions, and the maintenance of biodiversity. By analyzing bird images and the audio of birds, we can monitor bird distribution, abundance, and behavior to better understand the health of ecosystems. However, bird images and audio involve a vast amount of data. To improve the efficiency of data transmission and storage efficiency and save bandwidth, compressive sensing can overcome this challenge. Compressive sensing is a technique that uses the sparsity of signals to recover original data from a small number of linear measurements. This paper introduces a deep neural network based on the Iterative Shrinkage Thresholding Algorithm (ISTA) and a Convolutional Block Attention Module (CBAM), CBAM_ISTA-Net+, for the compressive reconstruction of bird images, audio Mel spectrograms and wavelet transform spectrograms. Using 45 bird species as research subjects, including 20 bird images, 15 audio-generated Mel spectrograms, and 10 audio wavelet transform (WT) spectrograms, the experimental results show that CBAM_ISTA-Net+ achieves a higher peak signal-to-noise ratio (PSNR) at different compression ratios. At a compression ratio of 50%, the average PSNR of the three datasets reaches 33.62 dB, 55.76 dB, and 38.59 dB, while both the Mel spectrogram and wavelet transform spectrogram achieve more than 30 dB at compression ratios of 25–50%. These results highlight the effectiveness of CBAM_ISTA-Net+ in maintaining high reconstruction quality even under significant compression, demonstrating its potential as a valuable tool for efficient data management in ecological research. [ABSTRACT FROM AUTHOR]

Image denoising is crucial for enhancing image quality, especially in medical applications where noise can significantly impact the accuracy of analysis and interpretation. This paper presents the development of an adaptive Gaussian filter-based denoising technique that effectively enhances images corrupted by various types of noise. By incorporating the adaptive adjustment of filter parameters based on local image characteristics, the proposed method achieves superior denoising performance. The algorithm analyzes the noisy image to estimate the noise characteristics, dynamically adjusting the Gaussian filter parameters to ensure optimal preservation of image details while effectively suppressing noise artifacts. Optimized strategies for parameter selection and filtering operations are employed to ensure computational efficiency. A comparative analysis demonstrates that the adaptive Gaussian filter outperforms traditional methods, achieving a higher Peak Signal-to-Noise Ratio (PSNR) and a lower Root Mean Square Error (RMSE). The technique also exhibits robustness against different noise distributions, making it a versatile solution for various image enhancement applications. These findings highlight the potential of the adaptive Gaussian filter to significantly improve image quality, facilitating more accurate and reliable analysis across diverse domains. [ABSTRACT FROM AUTHOR]

Facial recognition systems are pivotal in modern applications such as security, healthcare, and public services, where accurate identification is crucial. However, environmental factors, transmission errors, or deliberate obfuscations often degrade facial image quality, leading to misidentification and service disruptions. This study employs Generative Adversarial Networks (GANs) to address these challenges by reconstructing corrupted or occluded facial images with high fidelity. The proposed methodology integrates advanced GAN architectures, multi-scale feature extraction, and contextual loss functions to enhance reconstruction quality. Six experimental modifications to the GAN model were implemented, incorporating additional residual blocks, enhanced loss functions combining adversarial, perceptual, and reconstruction losses, and skip connections for improved spatial consistency. Extensive testing was conducted using Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM) to quantify reconstruction quality, alongside face detection validation using SFace. The final model achieved an average PSNR of 26.93 and an average SSIM of 0.90, with confidence levels exceeding 0.55 in face detection tests, demonstrating its ability to preserve identity and structural integrity under challenging conditions, including occlusion and noise. The results highlight that advanced GAN-based methods effectively restore degraded facial images, ensuring accurate face detection and robust identity preservation. This research provides a significant contribution to facial image processing, offering practical solutions for applications requiring high-quality image reconstruction and reliable facial recognition.

IntroductionWavelet thresholding techniques are crucial in mitigating noise in data communication and storage systems. In image processing, particularly in medical imaging like MRI, noise reduction is vital for improving visual quality and accurate analysis. While existing methods offer noise reduction, they often suffer from limitations like edge and texture loss, poor smoothness, and the need for manual parameter tuning.MethodsThis study introduces a novel adaptive wavelet thresholding technique for noise reduction in brain MRI. The proposed method utilizes a linear prediction factor to adjust the threshold adaptively. This factor leverages temporal information and features from both the original and noisy images to determine a weighted threshold. This dynamic thresholding approach aims to selectively reduce or eliminate noise coefficients while preserving essential image features.ResultsThe proposed method was rigorously evaluated against existing state-of-the-art noise reduction techniques. Experimental results demonstrate significant improvements in key performance metrics, including mean squared error (MSE), peak signal-to-noise ratio (PSNR), and structural similarity index (SSIM).DiscussionThe proposed adaptive thresholding technique effectively addresses the limitations of existing methods by providing a more efficient and accurate noise reduction approach. By dynamically adjusting the threshold based on image-specific characteristics, this method effectively preserves image details while effectively suppressing noise. These findings highlight the potential of the proposed method for enhancing the quality and interpretability of brain MRI images.