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The Internet of Medical Things (IoMT) links medical devices and wearable, enhancing healthcare. To secure sensitive patient data over the IoMT, encryption is vital to retain confidentiality, prevent tampering, ensure authenticity, and secure data transfer. The intricate neural network architecture of deep learning models adds a layer of complexity and non-linearity to the encryption process, rendering it highly resistant to plaintext attacks. Specifically, the Cycle_GAN network is used as the leading learning network. This work suggests deep learning-based encryption for medical images using Cycle_GAN, a Generative Adversarial Network. Cycle_GAN changes images without paired training data that improves quality and feature preservation. Unlike conventional image-to-image translation techniques, Cycle_GAN doesn’t require a dataset with corresponding input–output pairs. Traditional methods typically needs paired data to learn the mapping between input and output images. Paired data can be challenging to obtain, specifically in medical imaging where gathering and annotating data can be time-consuming, laborious and expensive. The use of Cycle_GAN overwhelms this constraint by using unpaired data, where the input and output images are not explicitly paired. This method ensures confidentiality, authenticity, and secure transfer. Cycle_GAN consists of two major components: a generator used to modify the images, and a discriminator used to distinguish between real and fake images. Further, the Binary-Cross Entropy loss function is employed to train the network for precise predictions. The experiments are carried out on skin cancer datasets. The results demonstrate high-level efficient, systematic and coherent encryption as compared with other modernized medical image encryption methods. The proposed technique offers several benefits, including efficient encryption and decryption and robustness against unauthorized access.

Removal of motion artifacts from medical images is essential, because the respiration by patient causes degradation in artifact removal. Therefore, getting an MRI without any artifacts and noise is challenging. Previous techniques failed to preserve the image quality during denoising, thereby affecting the classification accuracy. To overcome such challenges, two modules are considered in this work: classification and denoising. The classification process is successfully performed by Hybrid Deep autoencoder-convolutional neural network (DAE-CNN). It classifies the original images into motion artifacts, available and normal images. Then, the available artifacts are removed using cross guided bilateral filter (CGBF). The optimal parameters required to improve the CGBF performance are selected using the hybrid optimization algorithm. The filter's performance relies on the optimal spatial and range kernels selection. Finally, the classification and denoising-based results are evaluated using the BRATS 2015 dataset and achieved 95.84% accuracy and PSNR – 46.4, which is better than existing methods. [ABSTRACT FROM AUTHOR]

Although there has been a lot of progress in the general area of signal denoising, noise removal remains a very challenging problem in real-world communication systems. Denoising algorithms are typically used during the image preprocessing phase and are chosen based on the type of image, as a specific algorithm may work for a given noise but not for another one. Moreover, an algorithm can sometimes consider crucial information as being noise and eliminate it, hence the importance of careful selection and tuning of denoising algorithms. Denoising algorithms built on discrete wavelet transform decomposes signals into different frequency resolution levels. Thresholding is then applied to higher frequency components which generally correspond to noise to eliminate this one. In this paper, we review wavelet-based denoising methods and compare their performance based on metrics such as peak signal-to-noise ratio (PSNR) and Structural Similarity (SSIM). This work aims to find the best wavelet denoising algorithm using Peak these metrics. The common Matlab images such as cameraman, barbara, coins, and eight are used for our test. From these tests, the BM3DM_DWT method was found to be the simplest and most efficient for denoising. [ABSTRACT FROM AUTHOR]

Advancements in information and communication technology have facilitated diverse operational environments, spanning across financial to military sectors. However, these advancements carry an escalation in cybersecurity threats, potentially compromising user privacy and security. Among the various mechanisms introduced to mitigate these threats, data hiding methods stand out. These methods embed covert data within cover data, such as audio and video files, thereby providing an additional layer of security. In this study, we develop upon the existing data hiding techniques, enhancing their capacity to conceal varied sizes of covert data. Our proposed method leverages both right and bottom context pixels for a more nuanced data hiding approach. The effectiveness of this scheme is evaluated by quantifying the quality of the stego data, represented by the Peak Signal to Noise Ratio (PSNR) value. Our initial findings indicate that our method yields superior stego data quality, suggesting its potential to accommodate a larger volume of covert data while preserving the similarity between the cover and stego files. This study thus contributes to more robust and efficient data hiding techniques, bolstering cybersecurity measures in the face of increasing digital threats. [ABSTRACT FROM AUTHOR]

Compressive sensing (CS)-based image coding has been widely studied in the field of image processing. However, the CS-based image encoder has a significant gap in image reconstruction performance compared with the conventional image compression methods. In order to improve the reconstruction quality of CS-based image encoder, we proposed an image parallel block compressive sensing (BCS) coding scheme, which is based on discrete Cosine transform (DCT) sparse basis matrix and partial discrete Fourier transform (DFT) measurement matrix. In the proposed parallel BCS scheme, each column of an image block is sampled by the same DFT measurement matrix. Due to the complex property of DFT measurement matrix, the compressed image data is complex. Then, the real part and imaginary part of the resulting BCS data are quantized and transformed into two bit streams, respectively. At the reconstruction stage, the resulting two bit streams are transformed back into two real signals using inverse quantization operation. The resulting two real signals are combined into one complex signal, which is served as the input data of the CS reconstructed algorithm. The theoretical analysis based on minimum Frobenius norm method demonstrates that the proposed DFT measurement matrix outperforms the other conventional measurement matrices. The simulation results show that the reconstructed performance of the proposed DFT measurement matrix is better than that of the other conventional measurement matrices for the proposed parallel BCS. Specifically, we analyzed the impact of quantization on the reconstruction performance of CS. The experiment results show that the effect of the quantization on reconstruction performance in BCS framework can nearly be ignored. [ABSTRACT FROM AUTHOR]

Data security has become a basic need in today's world as it shifts more and more toward an all-online mode of operation. All sorts of data and information are now passed from one entity to another over a variety of transmission channels as well as stored on virtual online repositories, and a large chunk of that information is highly private. Over this stream of technological advancements, various data security techniques like encryption, data hiding, etc. have been developed to provide security while transmitting or storing data. This paper proposes an approach to hiding one image inside another image by using CNN in for feature extraction and processing. The proposed model works on the principles of the auto-encoder architecture and is made up of two modules one of which is responsible for the task of processing the secret image and extracting n features and the incorporating those features in the cover image ion such a manner that the cover image retains its original visual qualities and also the secret image gets hidden in it, that module is the Hiding network/Encoder. The other part of this network is the one responsible to then extract the hidden secret image and them create back to its original, this paper is the Reveal network/Decoder. The proposed model is implemented on ImageNet and Pascal-VOC datasets with images of different sizes. In order to judge the models ability to hide the image and to extract is back, PSNR and SSIM are used as performance evaluation Metrics. [ABSTRACT FROM AUTHOR]

The Internet of Medical Things (IoMT) links medical devices and wearable, enhancing healthcare. To secure sensitive patient data over the IoMT, encryption is vital to retain confidentiality, prevent tampering, ensure authenticity, and secure data transfer. The intricate neural network architecture of deep learning models adds a layer of complexity and non-linearity to the encryption process, rendering it highly resistant to plaintext attacks. Specifically, the Cycle_GAN network is used as the leading learning network. This work suggests deep learning-based encryption for medical images using Cycle_GAN, a Generative Adversarial Network. Cycle_GAN changes images without paired training data that improves quality and feature preservation. Unlike conventional image-to-image translation techniques, Cycle_GAN doesn't require a dataset with corresponding input–output pairs. Traditional methods typically needs paired data to learn the mapping between input and output images. Paired data can be challenging to obtain, specifically in medical imaging where gathering and annotating data can be time-consuming, laborious and expensive. The use of Cycle_GAN overwhelms this constraint by using unpaired data, where the input and output images are not explicitly paired. This method ensures confidentiality, authenticity, and secure transfer. Cycle_GAN consists of two major components: a generator used to modify the images, and a discriminator used to distinguish between real and fake images. Further, the Binary-Cross Entropy loss function is employed to train the network for precise predictions. The experiments are carried out on skin cancer datasets. The results demonstrate high-level efficient, systematic and coherent encryption as compared with other modernized medical image encryption methods. The proposed technique offers several benefits, including efficient encryption and decryption and robustness against unauthorized access. [ABSTRACT FROM AUTHOR]

The prime intention of super-resolution (SR) technique is to restore the high-resolution images from one or more low-resolution (LR) images. These images are captured from the same scene with different acquisition systems with different resolution. Because these acquisition systems, images are suffered for an ill-posed problem with low visualization and picture information. Therefore, in this paper, the zoom-based super-resolution approach is proposed for super-resolution of low resolute images which are acquired from different camera zoom-lens. In this approach, three LR images of the same static scene which are acquired using three distinct zoom factors are used. Learning-based SR technique is used to enhance the spatial resolution of these LR images. The training dataset comprises three sets of captured images which are LR images, an enhanced version of LR images-HR1 and enhanced version of HR1 images-HR2. High-frequency details of the super-resolute image are learned in form of the discrete cosine transform (DCT) coefficients of HR training images. Finally, the super-resolved versions of LR observations, captured at different zoom-factors, are combined. The experimental results show that this proposed approach can be applied to various types of natural images in grayscale as well as color. The experimental results also show that this proposed approach performs better than existing approaches.

In the last decade, the communication of images through the internet has increased. Due to the growing demands for data transfer through images, protection of data and safe communication is very important. For this purpose, many encryption techniques have been designed and developed. New and secured encryption schemes based on chaos theory have introduced methods for secure as well as fast communication. A modified image encryption process is proposed in this work with chaotic maps and orthogonal matrix in Hill cipher. Image encryption involves three phases. In the first phase, a chaotic Henon map is used for permuting the digital image. In the second phase, a Hill cipher is used whose encryption key is generated by an orthogonal matrix which further is produced from the equation of the plane. In the third phase, a sequence is generated by a chaotic tent map which is later XORed. Chaotic maps play an important role in the encryption process. To deal with the issues of fast and highly secured image processing, the prominent properties of non-periodical movement and non-convergence of chaotic theory play an important role. The proposed scheme is resistant to different attacks on the cipher image. Different tests have been applied to evaluate the proposed technique. The results of the tests such as key space analysis, key sensitivity analysis, and information entropy, histogram correlation of the adjacent pixels, number of pixel change rate (NPCR), peak signal to noise ratio (PSNR), and unified average changing intensity (UCAI) showed that our proposed scheme is an efficient encryption technique. The proposed approach is also compared with some state-of-the-art image encryption techniques. In the view of statistical analysis, we claim that our proposed encryption algorithm is secured. [ABSTRACT FROM AUTHOR]

Due to developments in the Internet and communication technology, the world has become an international parish in a digital era. The number of bits is generated, replicated, and transferred through the internet or mobile phones in the blink of an eye. Digital watermarking is implemented to assure and provide data authentication, copyright protection to such type of transmission. This paper presents a robust and imperceptible watermarking technique that is achieved with the combination of discrete cosine transform (DCT) and the genetic algorithm for color images. In this paper, DCT transform is based on dividing the cover image into 8X8 blocks, and a genetic algorithm is employed here for answering the optimization problem of digital watermarking as it plays a major role in obtaining an optimized solution. So main advantage of the proposed work can be described as the selection of the best pixel group for embedding so the quality of the watermarked image is better. Due to better pixels group selection for embedding the data losses is less during watermarking and we achieve better results. The purpose of optimization is to maximize the watermarking results in the presented watermarking method. In this work, various images have been imperiled to test the quality measurement in terms of standard metrics such as Peak Signal to Noise Ratio (PSNR), Normalized Cross-Correlation (NCC). Proposed work is compared with existing approach DCT and decision tree induction ID3. Further performance evaluation for different color models such as Red, Green, and Blue (RGB), YCbCr, YIQ color spaces have been calculated. Results of the composing approach are found better than the existing approach in terms of PSNR. In the red plane, the PSNR value is increased by 12%, 18% in the green plane, and 14% in the blue plane for image Lena. The proposed scheme achieves good imperceptibility and robustness against many watermarking attacks too. It can be applied to digital repositories and libraries, ownership authentication, and optimizing the parameters of watermarking in terms of PSNR and NCC. [ABSTRACT FROM AUTHOR]

The prime intention of super-resolution (SR) technique is to restore the high-resolution images from one or more low-resolution (LR) images. These images are captured from the same scene with different acquisition systems with different resolution. Because these acquisition systems, images are suffered for an ill-posed problem with low visualization and picture information. Therefore, in this paper, the zoom-based super-resolution approach is proposed for super-resolution of low resolute images which are acquired from different camera zoom-lens. In this approach, three LR images of the same static scene which are acquired using three distinct zoom factors are used. Learning-based SR technique is used to enhance the spatial resolution of these LR images. The training dataset comprises three sets of captured images which are LR images, an enhanced version of LR images-HR1 and enhanced version of HR1 images-HR2. High-frequency details of the super-resolute image are learned in form of the discrete cosine transform (DCT) coefficients of HR training images. Finally, the super-resolved versions of LR observations, captured at different zoom-factors, are combined. The experimental results show that this proposed approach can be applied to various types of natural images in grayscale as well as color. The experimental results also show that this proposed approach performs better than existing approaches. [ABSTRACT FROM AUTHOR]

We know that COVID-19 has been considered a pandemic and various types of symptoms are analyzed by the doctors. Various cases belonging to COVID-19 are asymptomatic and due to this fact the disease is not analyzed at an initial stage and the condition of the patient will be critical. So, the purpose of this work is to provide a solution that will find out the highly precise test result of COVID-19. Magnetic Resonance Imaging, CT Scan & Lung Ultrasound are some of the methods which can provide the exact results for the testing. But the problem associated with these imaging modalities is that they are time-consuming and the data provided by these modalities are large enough to store or transmit. A compression technique is required which can reduce the time as well as data size. Computed Tomography with Compressive Sensing (CS) Technique is used as an approach to tackle the above-stated problem. To analyze the fact that this technique is efficient, we consider the Computed Tomography-based Chest images of COVID-19 infected patients and apply the CS technique (Basis Pursuit) with Discrete Cosine Transform as a representation basis and Gaussian as a measurement matrix. As a result of this study, we find out three parameters, PSNR, SSIM & FSIM, to visualize the efficiency of the reconstruction strategy. This work concludes that the Computed Tomography approach with the help of CS can be used for fast and efficient imaging for COVID-19 as well as other diseases of the same kind. [ABSTRACT FROM AUTHOR]

Keeping in view the variety of the applications, image denoising still remains the unexplored territory for the researchers. There are many pros and cons in existing denoising algorithms. The two prime cons of image denoising algorithms are (i) Over and under detection of noisy pixels (ii) Low performance at high noise levels. So, in order to overcome these existing issues, a spatially adaptive image denoising via enhanced noise detection method (SAID-END) is proposed for grayscale and color images. The denoising is achieved using a two-stage sequential algorithm, the first stage ensures accurate noise estimation by eliminating over and under detection of noisy pixels. The second stage performs image restoration by considering non-noisy pixels in estimation of the original pixel value. To enhance the accuracy while denoising high-density impulse noise and artifacts, both noise estimation and restoration stages are using a spatially adaptive window (window expands to spatially connected area), the size of the window depends upon the noise level in the vicinity of the reference noisy pixel. The two stages of the proposed method are referred to as (i) Enhanced adaptive noise detection (ii) Non-corrupted pixel sensitive adaptive image restoration. The proposed method is evaluated by two test steps to ensure its versatility and robustness. In the first step, the proposed method is tested on a wide standard data set of color and grayscale images affected by impulse noise and artifacts. The results of proposed method are compared with well-known methods compatible for denoising impulse noise and artifacts. In the second step, the results of proposed method are compared with the recent state of the art algorithms for traditional test images. The result shows that the proposed method outperforms the existing denoising methods when applied to grayscale and color images.

In this paper, Framelet and Walsh transform are proposed for transformation, and then using arithmetic coding for compress an image. The goal is to achieve higher compression ratio by applying two levels Framelet transform (FLT), and then apply 2D Walsh-Hadamard transform (WHT) on each 8x8 block of the low frequency sub-band, while all other sub-bands are ignored. Experimental results show that the proposed algorithm gets best possible solution for tradeoff between compression ratio (size of image) and quality of compressed image, Peak Signal to Noise Ratio (PSNR). The simulation was carried using MATLAB software package version 2014. In this work, experiments were carried out on the gray scale and colored images.

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Medical Imaging and scanning technologies are used to provide better resolution of body and tissues. To achieve a better-quality Magnetic Resonance (MR) image with a minimum duration of processing time is a tedious task. So, our purpose in this paper is to find out a solution that can minimize the reconstruction time of an MRI signal. Compressive sensing can be used to accelerate Magnetic Resonance Image (MRI) acquisition by acquiring fewer data through the under-sampling of k-space, so it can be used to minimize the time. But according to the relaxation time, we can further classify the MRI signal into T1, T2, and Proton Density (PD) weighted images. These weighted images represent different signal intensities for different types of tissues and body parts. It also affects the reconstruction process conducted by using the Compressive Sensing Approach. This study is based on finding out the effect of T1, T2, and Proton Density (PD) weighted images on the reconstruction process as well as various image quality parameters like MSE, PSNR, & SSIM also calculated to analyze this effect. Meanwhile, we can analyze how many samples are enough to reconstruct the MR image so the problem associated with time and scanning speed can be reduced up to an extent. In this paper, we got the Structural Similarity Index Measure (SSIM) value up to 0.89 & PSNR value 37.83451 dB at an 85 % compression ratio for the T2 weighted image. [ABSTRACT FROM AUTHOR]

The secure communication of information is a major concern over the internet. The information must be protected before transmitting over a communication channel to avoid security violations. In this paper, a new hybrid method called compressed encrypted data embedding (CEDE) is proposed. In CEDE, the secret information is first compressed with Lempel Ziv Welch (LZW) compression algorithm. Then, the compressed secret information is encrypted using the Advanced Encryption Standard (AES) symmetric block cipher. In the last step, the encrypted information is embedded into an image of size 512 × 512 pixels by using image steganography. In the steganographic technique, the compressed and encrypted secret data bits are divided into pairs of two bits and pixels of the cover image are also arranged in four pairs. The four pairs of secret data are compared with the respective four pairs of each cover pixel which leads to sixteen possibilities of matching in between secret data pairs and pairs of cover pixels. The least significant bits (LSBs) of current and imminent pixels are modified according to the matching case number. The proposed technique provides doublefolded security and the results show that stego image carries a high capacity of secret data with adequate peak signal to noise ratio (PSNR) and lower mean square error (MSE) when compared with existing methods in the literature. [ABSTRACT FROM AUTHOR]

Now a days digital watermarking appeared as a solution for copyright detection, protection and maintenance of important data. This paper deals with a new reversible watermarking algorithm based on adaptive prediction error expansion, which can recover original image after extracting the hidden data. Embedding capacity of such algorithm depend on the prediction accuracy of the predictor. The method can embed secret data into 3×3 image block order by exploiting the pixel redundancy within each block. It has been observed that proposed method of reversible watermarking provide much better results in terms of Peak Signal to Noise Ratio (PSNR) and Mean Square Error (MSE) in comparison to existing literature.

Security, when referred to the context of data/information, is indeed a very vulnerable and necessary entity at present. Rigorous researches are cropping up for the betterment of the secured globe. Primarily, the term 'Steganography' has been in limelight to cater the extreme need of protection of sensitive and confidential data. This paper presents a two level steganographic approach of masking the secret data. This is done to facilitate data hiding and hence ensures a covert communication. On the whole, the targeted goal of this methodology is to maintain a trade-off between payload, imperceptibility and robustness. The first stage of the procedure imposes the Arnold transformation on the carrier image. The output of this stage is a scrambled image. This scrambling of pixel data bits disrupts the normal orientation of the resident pixels. Next, an N-puzzle based technique is applied on the scrambled image to promote a strategy of encryption. The concept of N-puzzle problem stands to be the base of this step. Post this stage, the output generated is a further encrypted image. Thereafter, the insertion technique of Mid Position Value (MPV) is applied to embed bits from the secret image within the above generated form of cover/carrier. After the procedure of insertion, the application of reverse N-puzzle encryption technique followed by the inverse Arnold transform fosters the final stego-image. This, on the whole, results in reverting back to the normal orientation of the original input image. All of the given experimental results highlight the outcome of the whole methodology. On this context, several of the quantitative as well as qualitative benchmark parameters have been analyzed. The results computed shows that the quality is well maintained. The generated stego is imperceptible. It also supports the non-detectability of secret data. The payload promoted in this procedure is quite high. Thus, the trade-off between the security parameters is maintained. [ABSTRACT FROM AUTHOR]

Multiple car detection and recognition using video surveillance system is a difficult problem in image processing. Multiple car detection helps to reduce traffic congestion's in highway, reduce road accidents, identifying suspicious vehicles, identifying people in crime scenes etc. There are several methods already existing in the field of vehicle detection, and most of them are highly expensive and unreliable. To overcome this, we have proposed more efficient and economic system which provides accurate results. For this, conversion of video into different frames has been carried out as the initial step, followed by effective preprocessing, which is a crucial part in image processing, it effectively improves all other processes that takes input from prepossessed data for further analysis. Therefore by making prepossessing steps more robust, we will be able to get more accurate and reliable outputs. After obtaining the pre-processed data, different segmentation methods has been applied, which will help in detection and recognition of vehicles in traffic. [ABSTRACT FROM AUTHOR]

As digital cameras become more enhanced and small, CCD sensors can relate to only one color of a pixel. This color mosaic pattern is called as Bayer Pattern(BP) which requires processing to obtain a color image with a higher resolution. Each image pixel that undergoes interpolation has a full color spectrum based on surrounding pixel colors. Here we introduce Adaptive CFA(ACFA) interpolation model. For normal image regions hue technique is used while edge regions adapt the new technique. It is proposed to apply fuzzy logic and fuzzy rule which is based on Genetic Algorithm that uses random local search to enhance the PSNR. Medical image reconstruction by this proposed fuzzy based method outperforms the other medical image reconstruction methods. [ABSTRACT FROM AUTHOR]