Pixel Value Differencing Research Articles

A low-dose CT image denoising method based on state space model

Abstract Low-dose CT medical imaging techniques introduce noise while reducing radiation risks, necessitating denoising processing. However, existing mainstream denoising methods face a difficult trade-off between preserving image detail information and accurately removing noise. To address this issue, we propose a low-dose CT image denoising method based on a state space model. Firstly, a dynamic edge information enhancement module is introduced to automatically extract edge information from images using a learnable LoG operator and fuse it into feature layers at different scales to suppress edge information loss caused by denoising processes. Secondly, a U-net encoder based on state space estimation is designed to dynamically model spatial relationships between pixels through neighborhood filtering, enabling consideration of local differences in pixel values during denoising and better preservation of edges and textures. Compared to existing denoising methods, our approach achieves stable noise removal in low-dose CT images while preserving the original texture structure.

A Method for Sorting High-Quality Fresh Sichuan Pepper Based on a Multi-Domain Multi-Scale Feature Fusion Algorithm.

Post-harvest selection of high-quality Sichuan pepper is a critical step in the production process. To achieve this, a visual system needs to analyze Sichuan pepper with varying postures and maturity levels. To quickly and accurately sort high-quality fresh Sichuan pepper, this study proposes a multi-scale frequency domain feature fusion module (MSF3M) and a multi-scale dual-domain feature fusion module (MS-DFFM) to construct a multi-scale, multi-domain fusion algorithm for feature fusion of Sichuan pepper images. The MultiDomain YOLOv8 Model network is then built to segment and classify the target Sichuan pepper, distinguishing the maturity level of individual Sichuan peppercorns. A selection method based on the average local pixel value difference is proposed for sorting high-quality fresh Sichuan pepper. Experimental results show that the MultiDomain YOLOv8-seg achieves an mAP50 of 88.8% for the segmentation of fresh Sichuan pepper, with a model size of only 5.84 MB. The MultiDomain YOLOv8-cls excels in Sichuan pepper maturity classification, with an accuracy of 98.34%. Compared to the YOLOv8 baseline model, the MultiDomain YOLOv8 model offers higher accuracy and a more lightweight structure, making it highly effective in reducing misjudgments and enhancing post-harvest processing efficiency in agricultural applications, ultimately increasing producer profits.

A New Reversible Data Hiding Method Using a Proportional Relation between Peak Signal-to-Noise Ratio and Embedding Capacity on Convolutional Neural Network

In the rapidly evolving fields of artificial intelligence and various industries, the secure processing and management of massive data have become paramount. This paper introduces an innovative reversible data hiding (RDH) method that leverages a Convolutional Neural Network (CNN)-based predictor to generate a predicted image from a given cover image. The secret data are ingeniously embedded within the differences in pixel values between the cover and predicted images. Our experimental analysis reveals a notable reduction in image distortion with increasing secret data size, showcasing the method’s potential for diverse applications. The unique aspect of our approach lies in the proportional relation between the Peak Signal-to-Noise Ratio (PSNR) and Embedding Capacity, highlighting its efficacy and efficiency in reversible data hiding.

Boundary Gaussian Distance Loss Function for Enhancing Character Extraction from High-Resolution Scans of Ancient Metal-Type Printed Books

This paper introduces a novel loss function, the boundary Gaussian distance loss, designed to enhance character segmentation in high-resolution scans of old metal-type printed documents. Despite various printing defects caused by low-quality printing technology in the 14th and 15th centuries, the proposed loss function allows the segmentation network to accurately extract character strokes that can be attributed to the typeface of the movable metal type used for printing. Our method calculates deviation between the boundary of predicted character strokes and the counterpart of the ground-truth strokes. Diverging from traditional Euclidean distance metrics, our approach determines the deviation indirectly utilizing boundary pixel-value difference over a Gaussian-smoothed version of the stroke boundary. This approach helps extract characters with smooth boundaries efficiently. Through experiments, it is confirmed that the proposed method not only smoothens stroke boundaries in character extraction, but also effectively eliminates noise and outliers, significantly improving the clarity and accuracy of the segmentation process.

Effectiveness of the Spatial Domain Techniques in Digital Image Steganography

Digital steganography is a new and extremely demanding method for transmitting information securely over the internet while employing a covert device. Since its inception in the 1990s till the present, digital steganography has a lengthy history. Early steganography focused primarily on imperceptibility, security and embedding capacity. In addition to using statistics as a foundation, convolution neural networks (CNN), generative adversarial networks (GAN), coverless approaches, and machine learning are all used to construct steganographic methods. Robustness is becoming a crucial component of many innovative techniques. Spatial, Transform, and Adaptive domains serve as the understructure of those novel methods. This broadens the range of steganographic technique development and often concentrates the implementation of adaptive techniques. As a result, this study helps to analyze the fundamentals of image steganography, a comparative review on the spatial domain algorithms. As using evaluation tools is strongly tied to the effectiveness of steganography, this study also goes into great detail about its application. The purpose of this research is to determine the best and most effective algorithm among the three competitive spatial domain algorithms, which are Least Significant Bit (LSB), Optimum Pixel Adjustment Procedure (OPAP), and Pixel Value Differencing (PVD) which in regard demonstrated the efficacy of spatial domain algorithms.

Image Steganography:

Steganography is the practice of hiding secret information within other media, such as images, audio, video, and text. It has become increasingly important in today's society as a way to enable private and secure communication. This research project focuses on image steganography techniques that are used to evade detection of the secret message through statistical steganalysis techniques. The aim of this research is to compare and evaluate different image steganography methods, study their implementation complexity, and propose a framework to improve current approaches. The research will provide a comparison of the efficiency of different steganography techniques in avoiding detection by steganalysis and may lead to the development of better steganography techniques in the future. This paper focuses on the three steganography methods in the spatial domain: Least Significant Bit (LSB), Pixel-Value Difference (PVD) and Edge-based Data Embedding (EBE) methods. A simple experiment has been conducted to encrypt several images using these three methods, and the distortion measure for LSB using mean square error (MSE) and peak-to-noise ratio (PSNR) has been investigated. Although the distortion measure result is considered acceptable for LSB method in the experiment, all methods resulted in significant difference in the file capacity. This shows that further enhancement in the security of the encryption is necessary so that the secret message will not be easily discovered. Thus, in this paper, we propose a conceptualized enhancement in the security using Morse Code, Base 64, SHA-245, and Advanced Encryption Standard (AES) before encrypting using PVD.

Secure and resilient improved image steganography using hybrid fuzzy neural network with fuzzy logic

The exponential growth in communication networks, data technology, advanced libraries, and mainly World Wide Web services has played a pivotal role in facilitating the retrieval of various types of information as needed. However, this progress has also led to security concerns related to the transmission of confidential data. Nevertheless, safeguarding these data during communication through insecure channels is crucial for obvious reasons. The emergence of steganography offers a robust approach to concealing confidential information, such as images, audio tracks, text files, and video files, in suitable media carriers. A novel technique is envisioned based on back-propagation learning. According to the proposed method, a hybrid fuzzy neural network (HFNN) is applied to the output obtained from the least significant bit substitution of secret data using pixel value differences and exploiting the modification direction. Through simulation and test results, it has been observed that the proposed methodology achieves secure steganography and superior visual quality. During the experiments, we observed that for the secret image of the cameraman, the PSNR & MSE values of the proposed technique are 61.963895 and 0.041361, respectively.

Image Steganography by Pixel-Value Differencing Using General Quantization Ranges

Image Steganography by Pixel-Value Differencing Using General Quantization Ranges

Self-adaptive Clothing Mapping based Virtual Try-on

Virtual Try-ON (VTON), as an innovative visual application in e-commerce scenarios with great commercial value, has been widely studied in recent years. Due to its better robustness and realistic effect, deformation-synthesize based VTON has become the dominant approach in this field. Existing clothing deformation techniques optimize the mapping relations between the original clothing image and the ground truth image of the worn clothing. However, there are color differences between the original and ground truth clothing images caused by lighting, warping and occlusion. The color differences may lead to misaligned clothing mapping by only minimizing the cost of pixel value difference. Another drawback is that taking the parsing prediction as ground truth will bring alignment remnant, rooting in the processing order of parsing and deformation. Aiming above two drawbacks, we put forward the Self-Adaptive clothing Mapping basEd Virtual Try-ON (SAME-VTON) for achieving realistic virtual try-on results as the same as real. The core of SAME-VTON is the self-adaptive clothing mapping technique, composed of two parts: a color-adaptive clothing mapping module and a parsing-adaptive prediction process. In the color-adaptive clothing mapping module, we map each pixel of the target clothing with a combination of multiple pixel values from the original clothing image, which considers both the position and color changes. Furthermore, different combination weights are learned to increase the diversity of color mapping. In the parsing-adaptive prediction process, the color-adaptive clothing mapping module is adopted to deform clothing firstly, then human parsing result is predicted under the reference of the deformed clothing, which can avoid alignment remnant. Extensive experiments demonstrate that the proposed SAME-VTON with self-adaptive clothing mapping technique can achieve optimal mapping in the case of large color differences and obtain superior results compared with existing deformation-synthesize based VTON.

Capacity-Raising Reversible Data Hiding Using Empirical Plus–Minus One in Dual Images

Electronic records of a patient’s health history are often shared among healthcare providers, and patient data must be kept secure to maintain the privacy of patients. One way of doing this is through data hiding, and this paper demonstrates a scheme to achieve this. This paper proposes a capacity-raising reversible data-hiding scheme using an empirical rules table in dual images. The aim of this research is to avoid drawing awareness to the transmission of information by providing a steganographic technique capable of embedding high-capacity data into an image while maintaining the good quality of the image. To hide the secret message(s), a rules table containing 13 entries is presented. This rules table is extendable to a table of up to 262,133 entries (with each entry containing one distinct character) that are related to the 13 entries in terms of the rules. The rules of this table are used during the embedding and extraction procedures. In the proposed method, 512 × 512 images are divided into 1 × 2 blocks where adjacent pixels are represented using x and y for both embedding and extraction, respectively. Recovery of the cover image from the stego image is also achievable during the extraction process. Conducted experiments show that the proposed method has an average pixel-to-signal noise ratio of 52.65 dB, which is higher than that achieved with the methods discussed in this paper. Additionally, the proposed method can embed a wider range of characters (depending on the image size) as compared to the rest of the methods, hence its high embedding capacity of 4.25 bpp. The proposed method can also withstand security attacks such as RS, pixel value difference, entropy, and chi-square attacks. The proposed method is also undetectable under visual attack analyses such as the difference histogram, pixel difference histogram, and visual inspection. Based on the higher embedding capacity, pixel-to-signal noise ratio, the ability of this method to be undetected under visual attack analysis, and the ability of this method to withstand security attacks, it can be concluded that the proposed method is superior to the other methods.

Enhanced Embedding Capacity for Data Hiding Approach Based on Pixel Value Differencing and Pixel Shifting Technology

Data hiding algorithms can achieve the issue of information security when secret data are transmitted via the public network. This paper proposes a novel data-hiding scheme based on pixel value differencing and pixel shifting to increase embedding capacity and visual quality. In the proposed method, the cover image is first divided into nonoverlapping blocks, and eight groups of different values are generated with the center pixel as the reference pixel to embed the secret message. The pixel shifting strategy is applied to adjust the stego image to improve its quality. Experimental results show that the proposed method has an embedding capacity of 740,000 bits with a peak signal-to-noise ratio value greater than 35 dB. Therefore, it is undetected by the human eye. Other compared state-of-the-art schemes have embedding capacities of 51,219 bits, 70,217 bits, and 104,055 bits, which are lower than the proposed methods’ 740,000 bits. The RS, chi-square, and rotation attack analyses prove that the proposed method can withstand security attacks. Thus, the proposed method performs better than other state-of-the-art methods with respect to embedding capacity and ability to withstand attacks.

Random embedded calibrated statistical blind steganalysis using cross validated support vector machine and support vector machine with particle swarm optimization

The evolvement in digital media and information technology over the past decades have purveyed the internet to be an effectual medium for the exchange of data and communication. With the advent of technology, the data has become susceptible to mismanagement and exploitation. This led to the emergence of Internet Security frameworks like Information hiding and detection. Examples of domains of Information hiding and detection are Steganography and steganalysis respectively. This work focus on addressing possible security breaches using Internet security framework like Information hiding and techniques to identify the presence of a breach. The work involves the use of Blind steganalysis technique with the concept of Machine Learning incorporated into it. The work is done using the Joint Photographic Expert Group (JPEG) format because of its wide use for transmission over the Internet. Stego (embedded) images are created for evaluation by randomly embedding a text message into the image. The concept of calibration is used to retrieve an estimate of the cover (clean) image for analysis. The embedding is done with four different steganographic schemes in both spatial and transform domain namely LSB Matching and LSB Replacement, Pixel Value Differencing and F5. After the embedding of data with random percentages, the first order, the second order, the extended Discrete Cosine Transform (DCT) and Markov features are extracted for steganalysis.The above features are a combination of interblock and intra block dependencies. They had been considered in this paper to eliminate the drawback of each one of them, if considered separately. Dimensionality reduction is applied to the features using Principal Component Analysis (PCA). Block based technique had been used in the images for better accuracy of results. The technique of machine learning is added by using classifiers to differentiate the stego image from a cover image. A comparative study had been during with the classifier names Support Vector Machine and its evolutionary counterpart using Particle Swarm Optimization. The idea of cross validation had also been used in this work for better accuracy of results. Further parameters used in the process are the four different types of sampling namely linear, shuffled, stratified and automatic and the six different kernels used in classification specifically dot, multi-quadratic, epanechnikov, radial and ANOVA to identify what combination would yield a better result.

Quantum Fruit Fly algorithm and ResNet50-VGG16 for medical diagnosis

Medical data are present in large amount and this is difficult to process for the diagnosis and Healthcare organization requires effective technique to handle big data. Existing techniques in medical diagnosis have limitations of imbalance data and overfitting problem. This research applies Quantum Fruit Fly Algorithm (QFFA) technique for feature selection to improve the effectiveness of classification in medical diagnosis. The Min–Max Normalization technique is applied to normalize the images to reduce the difference in pixel values and enhance the images. The ResNet50 and VGG16 deep learning models were applied for the feature extraction. The QFFA technique applies Archimedes spiral to increases the exploitation of the model to select unique features for classification. The Archimedes spiral provides spiral search in the top solutions of the Fruit Fly algorithm that helps to overcome local optima trap and increases exploitation. The QFFA technique selected features were applied to SVM model for the effective classification of medical diseases. The QFFA unique feature selection helps to overcome imbalance and overfitting problem in classification. The QFFA model has achieved better results in terms of various performance metrics such as sensitivity (99.26 %), and accuracy (99.04%) than existing Deep 1D-CNN and GA-Decision tree models.

Correction to: Enhanced adaptive data hiding method using LSB and pixel value differencing

Correction to: Enhanced adaptive data hiding method using LSB and pixel value differencing

CMS-NET: deep learning algorithm to segment and quantify the ciliary muscle in swept-source optical coherence tomography images.

The ciliary muscle plays a role in changing the shape of the crystalline lens to maintain the clear retinal image during near work. Studying the dynamic changes of the ciliary muscle during accommodation is necessary for understanding the mechanism of presbyopia. Optical coherence tomography (OCT) has been frequently used to image the ciliary muscle and its changes during accommodation in vivo. However, the segmentation process is cumbersome and time-consuming due to the large image data sets and the impact of low imaging quality. This study aimed to establish a fully automatic method for segmenting and quantifying the ciliary muscle on the basis of optical coherence tomography (OCT) images. A perspective cross-sectional study. In this study, 3500 signed images were used to develop a deep learning system. A novel deep learning algorithm was created from the widely used U-net and a full-resolution residual network to realize automatic segmentation and quantification of the ciliary muscle. Finally, the algorithm-predicted results and manual annotation were compared. For segmentation performed by the system, the total mean pixel value difference (PVD) was 1.12, and the Dice coefficient, intersection over union (IoU), and sensitivity values were 93.8%, 88.7%, and 93.9%, respectively. The performance of the system was comparable with that of experienced specialists. The system could also successfully segment ciliary muscle images and quantify ciliary muscle thickness changes during accommodation. We developed an automatic segmentation framework for the ciliary muscle that can be used to analyze the morphological parameters of the ciliary muscle and its dynamic changes during accommodation.

Study of steganography and data encryption algorithm by using alpha channels

A study of data encryption technologies by using steganography is carried out, the principles of operation and the basics of their application are considered. The main attention is paid to the principle of operation of alpha channels. The elements of cryptography and stegoanalysis, which are closely related to steganography, are described. An author’s method of data encryption by using alpha channels, which differs from the known analogues of LeastSignificantBit (LSB), PixelValueDifference (PVD), GreyLevelModification (GLM) is proposed. The proposed solution can be used in everyday life to hide the transfer of information and provide a secure way to transfer data. Keywords alpha channel, steganography, cryptography, stegoanalysis, eXclusive OR-encryption, cyber security, encryption

FPGA image encryption-steganography using a novel chaotic system with line equilibria

The wide dissemination of images makes its protection problems urgent. Chaos theory is widely applied since it provides better solutions to image protection. This paper introduces a novel chaotic system that has line equilibrium points. Chaotic signals generated by chaotic systems are used in encryption and steganography to enhance security. Besides, the scrambling-diffusion encryption system is combined with the least significant bit (LSB)-pixel value differencing (PVD) steganography algorithm. The proposed algorithm process is reversible and the extracted image is lossless. And the whole system is designed and implemented on a field programmable gate array (FPGA), which improves the embedding efficiency and achieves high throughput while ensuring good image quality.

PERBANDINGAN DAN KOMBINASI KINERJA ALGORITMA LEAST SIGNIFICANT BIT DENGAN PIXEL VALUE DIFFERENCING PADA STEGANOGRAFI CITRA WARNA

One of the most common problems in this digitalization era is data security issues, such as being compromised by digital data. Actually, there are many ways to protect digital data, one of which is by using steganography techniques. Where the steganography technique is a technique for inserting a secret message into an image media. In this study, we will discuss how the performance of two teganographic methods, namely Least Significant Bit (LSB) and Pixel Value Differencing (PVD) and how the results if the two methods are combined in implementing color image steganography. To determine the quality of the method studied, MSE, PSNR, and SSIM were used as testing methods. As a result, the PVD algorithm on the red tifanny layer image obtained an MSE value of 0.051, PSNR 60,974, and SSIM 0.998. For the LSB algorithm, the flax image in the green layer obtained MSE values of 0.0039, PSNR 72.218, and SSIM 1.000. Finally, the combination algorithm between PVD and LSB on tifanny's image in the red layer obtained an MSE value of 0.054, PSNR 60,734, and SSIM 0.998.

Iron-based coupling media for MRI-guided ultrasound surgery.

In this study, we examine the effects of a recently developed, iron-based coupling medium (IBCM) on guidance magnetic resonance (MR) scans during transcranial, magnetic-resonance-guided, focused ultrasound surgery (tMRgFUS) procedures. More specifically, this study tests the hypotheses that the use of the IBCM will (a) not adversely affect image quality, (b) remove aliasing from small field-of-view scans, and (c) decouple image quality from the motion state of the coupling fluid. An IBCM, whose chemical synthesis and characterization are reported elsewhere, was used as a coupling medium during tMRgFUS procedures on gel phantoms. Guidance magnetization-prepared rapid-gradient-echo (MP-RAGE), TSE, and GRE scans were acquired with fields of view of 28 and 18cm. Experiments were repeated with the IBCM in several distinct flow states. GRE scans were used to estimate temperature time courses as a gel target was insonated. IBCM performance was measured by computing (i) the root mean square difference (RMSD) of TSE and GRE pixel values acquired using water and the IBCM, relative to the use of water; (ii) through-time temperature uncertainty for GRE scans; and (iii) Bland-Altman analysis of the temperature time courses. Finally, guidance TSE and GRE scans of a human volunteer were acquired during a separate sham tMRgFUS procedure. As a control, all experiments were repeated using a water coupling medium. Use of the IBCM reduced RMSD in TSE scans by a factor of 4 or more for all fields of view and nonstationary motion states, but did not reduce RMSD estimates in MP-RAGE scans. With the coupling media in a stationary state, the IBCM altered estimates of temperature uncertainty relative to the use of water by less than 0.2°C. However, with a high flow state, the IBCM reduced temperature uncertainties by the statistically significant amounts (at the 0.01 level) of 0.5°C (28cm field of view) and 5°C (18cm field of view). Bland-Altman analyses found a 0.1°C ± 0.5°C difference between temperature estimates acquired using water and the IBCM as coupling media. Finally, scans of a human volunteer using the IBCM indicate more conspicuous grey/white matter contrast, a reduction in aliasing, and a less than 0.2°C change in temperature uncertainty. The use of an IBCM during tMRgFUS procedures does not adversely affect image quality for TSE and GRE scans, can decouple image quality from the motion state of the coupling fluid, and can remove aliasing from scans where the field of view is set to be much smaller than the spatial extent of the coupling fluid.

Feature Weighted Attention—Bidirectional Long Short Term Memory Model for Change Detection in Remote Sensing Images

In remote sensing images, change detection (CD) is required in many applications, such as: resource management, urban expansion research, land management, and disaster assessment. Various deep learning-based methods were applied to satellite image analysis for change detection, yet many of them have limitations, including the overfitting problem. This research proposes the Feature Weighted Attention (FWA) in Bidirectional Long Short-Term Memory (BiLSTM) method to reduce the overfitting problem and increase the performance of classification in change detection applications. Additionally, data usage and accuracy in remote sensing activities, particularly CD, can be significantly improved by a large number of training models based on BiLSTM. Normalization techniques are applied to input images in order to enhance the quality and reduce the difference in pixel value. The AlexNet and VGG16 models were used to extract useful features from the normalized images. The extracted features were then applied to the FWA-BiLSTM model, to give more weight to the unique features and increase the efficiency of classification. The attention layer selects the unique features that help to distinguish the changes in the remote sensing images. From the experimental results, it was clearly shown that the proposed FWA-BiLSTM model achieved better performance in terms of precision (93.43%), recall (93.16%), and overall accuracy (99.26%), when compared with the existing Difference-enhancement Dense-attention Convolutional Neural Network (DDCNN) model.