Efficient three layer secured adaptive video steganography method using chaotic dynamic systems.

In light of the unprecedented growth in internet usage, safeguarding data from unauthorized access has emerged as a paramount concern. Cryptography and steganography stand as pivotal methods for ensuring data security during transmission. This study introduces an innovative adaptive video steganography approach featuring three tiers of security for extracting concealed information, thereby facilitating secure communication. The embedding process operates within the spatial domain of cover video frames, enabling a remarkable hiding ratio of up to 28.125% (equivalent to 2.25 bits per pixel in payload) without compromising the quality of video frames. Users are afforded the flexibility to select between partial or full embedding capacity of CVF through the proposed adaptive control block (ACB). The chaotic key generator (CKG), which combines a logistic map and sine map, is employed to generate highly sensitive initial seeds for permutation order (PO), frame selection (FS), and random position for hiding (RPH), thereby ensuring three levels of security. Prior to transmission, both CVF and hidden data (SD) are encrypted using PO. Encrypted CVFs are then randomly selected using FS for embedding, with RPH employed during the embedding process. Subsequently, for transmitting the stego-video frame, embedded CVFs are decrypted using the same PO. Experimental results demonstrate the efficacy of the proposed approach, achieving an adaptive hiding ratio ranging from 7.1 to 28.125% (equivalent to 0.56 to 2.25 bits per pixel in payload) and maintaining a peak signal-to-noise ratio (PSNR) within the range of 50.25 to 62.05 dB.

SM-CycleGAN: crop image data enhancement method based on self-attention mechanism CycleGAN.

Crop disease detection and crop baking stage judgement require large image data to improve accuracy. However, the existing crop disease image datasets have high asymmetry, and the poor baking environment leads to image acquisition difficulties and colour distortion. Therefore, we explore the potential of the self-attention mechanism on crop image datasets and propose an innovative crop image data-enhancement method for recurrent generative adversarial networks (GANs) fused with the self-attention mechanism to significantly enhance the perception and information capture capabilities of recurrent GANs. By introducing the self-attention mechanism module, the cycle-consistent GAN (CycleGAN) is more adept at capturing the internal correlations and dependencies of image data, thus more effectively capturing the critical information among image data. Furthermore, we propose a new enhanced loss function for crop image data to optimise the model performance and meet specific task requirements. We further investigate crop image data enhancement in different contexts to validate the performance and stability of the model. The experimental results show that, the peak signal-to-noise ratio of the SM-CycleGAN for tobacco images and tea leaf disease images are improved by 2.13% and 3.55%, and the structural similarity index measure is improved by 1.16% and 2.48% compared to CycleGAN, respectively.

An Improved 3D OPC Method for the Fabrication of High-Fidelity Micro Fresnel Lenses.

Based on three-dimensional optical proximity correction (3D OPC), recent advancements in 3D lithography have enabled the high-fidelity customization of 3D micro-optical elements. However, the micron-to-millimeter-scale structures represented by the Fresnel lens design bring more stringent requirements for 3D OPC, which poses significant challenges to the accuracy of models and the efficiency of algorithms. Thus, a lithographic model based on optical imaging and photochemical reaction curves is developed in this paper, and a subdomain division method with a statistics principle is proposed to improve the efficiency and accuracy of 3D OPC. Both the simulation and the experimental results show the superiority of the proposed 3D OPC method in the fabrication of Fresnel lenses. The computation memory requirements of the 3D OPC are reduced to below 1%, and the profile error of the fabricated Fresnel lens is reduced 79.98%. Applying the Fresnel lenses to an imaging system, the average peak signal to noise ratio (PSNR) of the image is increased by 18.92%, and the average contrast of the image is enhanced by 36%. We believe that the proposed 3D OPC method can be extended to the fabrication of vision-correcting ophthalmological lenses.

Spatial and Channel Aggregation Network for Lightweight Image Super-Resolution.

Advanced deep learning-based Single Image Super-Resolution (SISR) techniques are designed to restore high-frequency image details and enhance imaging resolution through the use of rapid and lightweight network architectures. Existing SISR methodologies face the challenge of striking a balance between performance and computational costs, which hinders the practical application of SISR methods. In response to this challenge, the present study introduces a lightweight network known as the Spatial and Channel Aggregation Network (SCAN), designed to excel in image super-resolution (SR) tasks. SCAN is the first SISR method to employ large-kernel convolutions combined with feature reduction operations. This design enables the network to focus more on challenging intermediate-level information extraction, leading to improved performance and efficiency of the network. Additionally, an innovative 9 × 9 large kernel convolution was introduced to further expand the receptive field. The proposed SCAN method outperforms state-of-the-art lightweight SISR methods on benchmark datasets with a 0.13 dB improvement in peak signal-to-noise ratio (PSNR) and a 0.0013 increase in structural similarity (SSIM). Moreover, on remote sensing datasets, SCAN achieves a 0.4 dB improvement in PSNR and a 0.0033 increase in SSIM.

Double layer steganography technique using DNA sequences and images.

Information security has become increasingly challenging as a result of massive advancements in information and communication technologies. Due to the necessity of exchanging private information and the open nature of the network, there is an increased risk of various types of attacks. Consequently, data security is an essential component of data communication. One of the most effective methods used to achieve secrecy is steganography. This method hides data within a cover object without raising suspicion. The level of security is improved when two steganography methods are combined. This approach is known as multilevel steganography, which hides sensitive data in two cover objects in order to provide a two-level security system. Accordingly, we developed a technique that focuses on protecting secrecy while also being robust to attacks. The new technique uses a multi-layer steganography mechanism by using DNA sequences and images as carriers for sensitive data. The technique intends to hide secret messages in the DNA using the substation algorithm, and then the fake DNA is embedded in an image utilizing the discrete cosine transform (DCT) method. Eventually, the stego image is sent to the intended recipient. Different types of images with different sizes and lengths of messages and DNA sequences were used during the experiments. The results show that the proposed mechanism is resistant to histogram and chi-square attacks. The maximum mean value observed was 0.05, which means the histograms of the original and stego images are nearly identical, and the stego image does not raise any suspicion regarding the existence of secret information. In addition, the imperceptibility ratios were good, as the highest PSNR and MSE values were 0.078 and 72.2, respectively. Finally, the PNG and BMP images show excellent results. On the other hand, the JPG images failed to meet the expected ratio of imperceptibility and security.

Improving the brain image resolution of generalized q-sampling MRI revealed by a three-dimensional CNN-based method.

Background: Understanding neural connections facilitates the neuroscience and cognitive behavioral research. There are many nerve fiber intersections in the brain that need to be observed, and the size is between 30 and 50 nanometers. Improving image resolution has become an important issue for mapping the neural connections non-invasively. Generalized q-sampling imaging (GQI) was used to reveal the fiber geometry of straight and crossing. In this work, we attempted to achieve super-resolution with a deep learning method on diffusion weighted imaging (DWI). Materials and methods: A three-dimensional super-resolution convolutional neural network (3D SRCNN) was utilized to achieve super-resolution on DWI. Then, generalized fractional anisotropy (GFA), normalized quantitative anisotropy (NQA), and the isotropic value of the orientation distribution function (ISO) mapping were reconstructed using GQI with super-resolution DWI. We also reconstructed the orientation distribution function (ODF) of brain fibers using GQI. Results: With the proposed super-resolution method, the reconstructed DWI was closer to the target image than the interpolation method. The peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) were also significantly improved. The diffusion index mapping reconstructed by GQI also had higher performance. The ventricles and white matter regions were much clearer. Conclusion: This super-resolution method can assist in postprocessing low-resolution images. With SRCNN, high-resolution images can be effectively and accurately generated. The method can clearly reconstruct the intersection structure in the brain connectome and has the potential to accurately describe the fiber geometry on a subvoxel scale.

A Lightweight Low-dose PET Image Super-resolution Reconstruction Method based on Convolutional Neural Network.

BACKGROUND: PET imaging is one of the most widely used neurological disease screening and diagnosis techniques. AIMS: Since PET involves the radiation and tolerance of different people, the improvement that has always been focused on is to cut down radiation, in the meantime, ensuring that the generated images with low-dose tracer and generated images with standard-dose tracer have the same details of images. METHODS: We propose a lightweight low-dose PET super-resolution network (SRPET-Net) based on a convolutional neural network. In this research, We propose a method for accurately recovering highresolution (HR) PET images from low-resolution (LR) PET images. The network learns the details and structure of the image between low-dose PET images and standard-dose PET images and, afterward, reconstructs the PET image by the trained network model. RESULTS: The experiments indicate that the SRPET-Net can achieve a superior peak signal-to-noise ratio (PSNR) and structural similarity index measurement (SSIM) values. Moreover, our method has less memory consumption and lower computational cost. CONCLUSION: In our follow-up work, the technology can be applied to medical imaging in many different directions.

Flexible Thin Film Functionalized by Initiative Dust Removal and Anti-Fogging for Optical Device Applications.

The deposition of dust and condensation of fog will block the scattering and transmission of light, thus affecting the performance of optical devices. In this work, flexible polyethylene terephthalate (PET) foil functionalized by active dust removal and anti-fogging characteristics is realized which combines electrodynamic screen (EDS) and electro-heating devices. In lieu of traditional measurement methods of dust removal efficiency, the PSNR is employed to characterize the dust removal efficiency of the film for the first time. The results show that both dust removal and anti-fogging improve the image quality, in which the dust removal increases the PSNR from 28.1 dB to 34.2 dB and the anti-fogging function realizes a film temperature rise of 16.7 ∘C in 5 min, reaching a maximum of 41.3 ∘C. According to the high sensitivity of the PSNR, we propose a fully automatic CIS film-driven algorithm, and its feasibility has been demonstrated.

An efficient and secure technique for image steganography using a hash function.

Steganography is a technique in which a person hides information in digital media. The message sent by this technique is so secret that other people cannot even imagine the information's existence. This article entails developing a mechanism for communicating one-on-one with individuals by concealing information from the rest of the group. Based on their availability, digital images are the most suited components for use as transmitters when compared to other objects available on the internet. The proposed technique encrypts a message within an image. There are several steganographic techniques for hiding hidden information in photographs, some of which are more difficult than others, and each has its strengths and weaknesses. The encryption mechanism employed may have different requirements depending on the application. For example, certain applications may require complete invisibility of the key information, while others may require the concealment of a larger secret message. In this research, we proposed a technique that converts plain text to ciphertext and encodes it in a picture using up to the four least significant bit (LSB) based on a hash function. The LSBs of the image pixel values are used to substitute pieces of text. Human eyes cannot predict the variation between the initial Image and the resulting image since only the LSBs are modified. The proposed technique is compared with state-of-the-art techniques. The results reveal that the proposed technique outperforms the existing techniques concerning security and efficiency with adequate MSE and PSNR.

Early Glaucoma Detection by Using Style Transfer to Predict Retinal Nerve Fiber Layer Thickness Distribution on the Fundus Photograph.

Objective: We aimed to develop a deep learning (DL)-based algorithm for early glaucoma detection based on color fundus photographs that provides information on defects in the retinal nerve fiber layer (RNFL) and its thickness from the mapping and translating relations of spectral domain OCT (SD-OCT) thickness maps. Design: Developing and evaluating an artificial intelligence detection tool. Subjects: Pretraining paired data of color fundus photographs and SD-OCT images from 189 healthy participants and 371 patients with early glaucoma were used. Methods: The variational autoencoder (VAE) network training architecture was used for training, and the correlation between the fundus photographs and RNFL thickness distribution was determined through the deep neural network. The reference standard was defined as a vertical cup-to-disc ratio of ≥0.7, other typical changes in glaucomatous optic neuropathy, and RNFL defects. Convergence indicates that the VAE has learned a distribution that would enable us to produce corresponding synthetic OCT scans. Main Outcome Measures: Similarly to wide-field OCT scanning, the proposed model can extract the results of RNFL thickness analysis. The structural similarity index measure (SSIM) and peak signal-to-noise ratio (PSNR) were used to assess signal strength and the similarity in the structure of the color fundus images converted to an RNFL thickness distribution model. The differences between the model-generated images and original images were quantified. Results: We developed and validated a novel DL-based algorithm to extract thickness information from the color space of fundus images similarly to that from OCT images and to use this information to regenerate RNFL thickness distribution images. The generated thickness map was sufficient for clinical glaucoma detection, and the generated images were similar to ground truth (PSNR: 19.31 decibels; SSIM: 0.44). The inference results were similar to the OCT-generated original images in terms of the ability to predict RNFL thickness distribution. Conclusions: The proposed technique may aid clinicians in early glaucoma detection, especially when only color fundus photographs are available.

Feature Extraction of 3T3 Fibroblast Microtubule Based on Discrete Wavelet Transform and Lucy-Richardson Deconvolution Methods.

Accompanied by the increasing requirements of the probing micro/nanoscopic structures of biological samples, various image-processing algorithms have been developed for visualization or to facilitate data analysis. However, it remains challenging to enhance both the signal-to-noise ratio and image resolution using a single algorithm. In this investigation, we propose a composite image processing method by combining discrete wavelet transform (DWT) and the Lucy-Richardson (LR) deconvolution method, termed the DWDC method. Our results demonstrate that the signal-to-noise ratio and resolution of live cells' microtubule networks are considerably improved, allowing the recognition of features as small as 120 nm. The method shows robustness in processing the high-noise images of filament-like biological structures, e.g., the cytoskeleton networks captured by fluorescent microscopes.

An Effective Color Image Encryption Based on Henon Map, Tent Chaotic Map, and Orthogonal Matrices.

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.

Reversible Data Hiding in Encrypted Image Based on (7, 4) Hamming Code and UnitSmooth Detection.

With the development of cloud storage and privacy protection, reversible data hiding in encrypted images (RDHEI) plays the dual role of privacy protection and secret information transmission. RDHEI has a good application prospect and practical value. The current RDHEI algorithms still have room for improvement in terms of hiding capacity, security and separability. Based on (7, 4) Hamming Code and our proposed prediction/ detection functions, this paper proposes a Hamming Code and UnitSmooth detection based RDHEI scheme, called HUD-RDHEI scheme for short. To prove our performance, two database sets-BOWS-2 and BOSSBase-have been used in the experiments, and peak signal to noise ratio (PSNR) and pure embedding rate (ER) are served as criteria to evaluate the performance on image quality and hiding capacity. Experimental results confirm that the average pure ER with our proposed scheme is up to 2.556 bpp and 2.530 bpp under BOSSBase and BOWS-2, respectively. At the same time, security and separability is guaranteed. Moreover, there are no incorrect extracted bits during data extraction phase and the visual quality of directly decrypted image is exactly the same as the cover image.

Minimal Block Knight's Tour and Edge with LSB Pixel Replacement Based Encrypted Image Steganography.

The data security of an information is predominant in the digital world and gaining lot of importance. Cryptography and steganography are widely used in providing security to an information. In the proposed algorithm, the image encryption and steganography are performed using Knight's move in the game of chess called Knight's Tour Algorithm. Minimum block or square required for a knight's tour to reach all the squares is 5 × 5 block. The 5 × 5 blocks' pattern generated is used for image encryption. The encrypted image is then embedded into another image and block shuffling is performed to obtain a crypto-stego image. Proposed algorithm is robust and provides high data security with a good PSNR and SSIM.

Denoising Medical Images Using Machine Learning, Deep Learning Approaches: A Survey.

OBJECTIVE: Several denoising methods for medical images have been applied, such as Wavelet Transform, CNN, linear and Non-linear methods. METHODS: In this paper, A median filter algorithm will be modified and the image denoising method to wavelet transform and Non-local means (NLM), deep convolutional neural network (Dn- CNN), Gaussian noise, and Salt and pepper noise used in the medical image is explained. RESULTS: PSNR values of the CNN method are higher and showed better results than different filters (Adaptive Wiener filter, Median filter, and Adaptive Median filter, Wiener filter). CONCLUSION: Denoising methods performance with indices SSIM, PSNR, and MSE have been tested, and the results of simulation image denoising are also presented in this article.

Low-Complexity Rate-Distortion Optimization of Sampling Rate and Bit-Depth for Compressed Sensing of Images.

Compressed sensing (CS) offers a framework for image acquisition, which has excellent potential in image sampling and compression applications due to the sub-Nyquist sampling rate and low complexity. In engineering practices, the resulting CS samples are quantized by finite bits for transmission. In circumstances where the bit budget for image transmission is constrained, knowing how to choose the sampling rate and the number of bits per measurement (bit-depth) is essential for the quality of CS reconstruction. In this paper, we first present a bit-rate model that considers the compression performance of CS, quantification, and entropy coder. The bit-rate model reveals the relationship between bit rate, sampling rate, and bit-depth. Then, we propose a relative peak signal-to-noise ratio (PSNR) model for evaluating distortion, which reveals the relationship between relative PSNR, sampling rate, and bit-depth. Finally, the optimal sampling rate and bit-depth are determined based on the rate-distortion (RD) criteria with the bit-rate model and the relative PSNR model. The experimental results show that the actual bit rate obtained by the optimized sampling rate and bit-depth is very close to the target bit rate. Compared with the traditional CS coding method with a fixed sampling rate, the proposed method provides better rate-distortion performance, and the additional calculation amount amounts to less than 1%.

Adaptive Wavelet Based MRI Brain Image De-noising.

This paper presents a unique approach for wavelet-based MRI brain image de-noising. Adaptive soft and hard threshold functions are first proposed to improve the results of standard soft and hard threshold functions for image de-noising in the wavelet domain. Then, we applied the newly emerged improved adaptive generalized Gaussian distributed oriented threshold function (improved AGGD) on the MRI images to improve the results of the adaptive soft and hard threshold functions and also to display, this non-linear and data-driven function can work promisingly even in de-noising the medical images. The most important characteristic of this function is that it is dependent on the image since it is combined with an adaptive generalized Gaussian distribution function.Traditional thresholding neural network (TNN) and optimized based noise reduction have good results but fail to keep the visual quality and may blur some parts of an image. In TNN and optimized based image de-noising, it was required to use Least-mean-square (LMS) learning and optimization algorithms, respectively to find the optimum threshold value and parameters of the threshold functions which was time consuming. To address these issues, the improved AGGD based image de-noising approach is introduced to enhance the qualitative and quantitative performance of the above mentioned image de-noising techniques. De-noising using improved AGGD threshold function provides better results in terms of Peak Signal to Noise Ratio (PSNR) and also faster processing time since there is no need to use any Least-mean-square (LMS) learning and optimization algorithms for obtaining the optimum value and parameters of the thresholding functions. The experimental results indicate that image de-noising using improved AGGD threshold performs pretty well comparing with the adaptive threshold, standard threshold, improved wavelet threshold, and the optimized based noise reduction methods.

A new image steganography method with optimum pixel similarity for data hiding in medical images.

Steganography is one of the approaches used in data hiding. Image steganography, is a type of steganography that the image is used as a covering object. Data hiding capacity and image quality of the cover object are important factors in image steganography. Because the deterioration of image quality can be noticed by the human vision system, it attracts the attention of attackers. Therefore, the purpose of this study is increasing the amount of data to be hidden and stego image is to ensure high image quality. In the study, a new optimization-based method has been proposed by making use of the similarities of the pixels. In order to test the performance of the proposed method has been used visual quality analysis metrics such as MSE, RMSE, PSNR, SSIM and UQI. As a cover object; different sizes medical images have been used that obtained from the open access Dicom library database. Doctor comments in different capacities have been hidden to the medical images. Experimental results show that the average PSNR value is 66.5374, 59.4420 and 56.3936, respectively, when 1000 characters, 5000 characters and 10,000 characters data is hidden in 512 × 512 images. In addition, the average PSNR value is 60.4308, 53.3529 and 47.4113, respectively, when 1000 characters, 5000 characters and 10,000 characters data is hidden in 256 × 256 images. 10,000 characters of data have not been hidden in 256 × 256 images without using data compression techniques with classical similarity based LSB method. In the proposed method, 10,000 characters of data have been hidden in 256 × 256 size images without using data compression techniques.

Comparison of spatial temporal representations of the vectorcardiogram using digital image processing.

INTRODUCTION: The vectorcardiography (VCG) is a method of representing the heart's electrical activity in three dimensions that is not frequently used in clinical practice due to the higher complexity compared to electrocardiography (ECG). A way around this problem was the development of regression techniques to obtain the VCG from the 12­lead ECG and the evaluation of these techniques is done by comparing the parameters obtained by the gold standard method and by the VCG obtained by the alternative methods. In this paper it is proposed instead a comparison between the images of the VCG planes using the values returned by digital image processing metrics such as PSNR, SSIM and PW-SSIM. METHODS: The signals used were obtained from the Physikalisch-Technische Bundesanstalt Diagnostic ECG Database, which contains both the VCGs obtained by the gold standard method and the 12 lead ECG signals. They were divided into five groups that contained a control group and according to the region of the wall infarction. The ECG signals were then filtered using a Butterworth Finite Impulse Response bandpass filter, with cutoff frequencies of 3 Hz and 45 Hz and then the VCGs were by a computer application using the Kors inverse matrix method, the Kors quasi-orthogonal method and the Dower Inverse Matrix method. The reconstructed signals were then compared using the PSNR, SSIM and PW-SSIM methods. The returned values were presented in tables for each group containing the average value and standard deviance for each method in each VCG plane. RESULTS: Using image processing techniques, it was possible to perceive that the alternative methods to obtain the VCG have a high confiability that could be compared to the gold standard in signals from healthy subjects. However, signals from pathological subjects present variations that could be caused by a deficit of these alternative methods to represent the pathology in these cases. Considering the PW-SSIM, the Frontal plane by the reconstructions was considered the most similar to the gold standard, having PW-SSIM values higher than 0.93 and for the Horizontal plane two groups had PW-SSIM values lower than 0.90 and for the Sagittal plane all groups had values lower than this value. DISCUSSION: The values yielded by the PSNR and SSIM had low variance, worsening the perception of the effect of the reconstruction method used or the infarction effect over the reconstruction. The values lower than 0.90 could indicate that these planes have their generation most affected by the infarction. CONCLUSION: The three methods of obtaining the VCG Frank leads, the Kors Quasi-Orthogonal method, the Kors Linear Regression method and the Dower Inverse Matrix, presented differences in the metrics: PSNR, SSIM and PW-SSIM in normal subjects according to the planes frontal, horizontal and sagittal and in subjects with Myocardial Infarction according to its topography: anterior, inferolateral, inferior or multiarterials. Considering only the PW-SSIM, the QO method had the best performance in different signals, followed by the Dower method.

USDL: Inexpensive Medical Imaging Using Deep Learning Techniques and Ultrasound Technology.

In this study, we present USDL, a novel model that employs deep learning algorithms in order to reconstruct and enhance corrupted ultrasound images. We utilize an unsupervised neural network called an autoencoder which works by compressing its input into a latent-space representation and then reconstructing the output from this representation. We trained our model on a dataset that compromises of 15,700 in vivo images of the neck, wrist, elbow, and knee vasculature and compared the quality of the images generated using the structural similarity index (SSIM) and peak to noise ratio (PSNR). In closely simulated conditions, the architecture exhibited an average reconstruction accuracy of 90% as indicated by our SSIM. Our study demonstrates that USDL outperforms state of the art image enhancement and reconstruction techniques in both image quality and computational complexity, while maintaining the architecture efficiency.