Correlation Of Pixels Research Articles

Chaotic system constructed by product trigonometric function and polynomial and applied to color image encryption

In the applied research of nonlinear system, the low degree of chaos in the dynamical system leads to the limitation of using the chaos method to solve some practical problems. In this paper, we use the product trigonometric function and ternary polynomial to build a dynamical system, which has strong chaotic characteristics. The dynamical system is constructed by two product trigonometric functions and a ternary linear equation, and its chaotic properties are verified by bifurcation diagrams, Lyapunov exponents, fractal dimensions, etc. The system has many parameters and large parameter intervals and is not prone to cycles. The conditions for the non-divergence of this system are given by mathematical derivation, and it is found that the linear part of the system can be replaced by an arbitrary ternary polynomial system and still not diverge, and the bifurcation diagram is drawn to verify it. Finally, the chaotic sequence is distributed more uniformly in the value domain space by adding the modulo operation. Then, the bit matrix of multiple images is directly permuted by the above system, and the experiment confirms that the histogram, information entropy, and pixel correlation of its encrypted images are satisfactory, as well as a very large key space.

Image Processing Method Based on Chaotic Encryption and Wavelet Transform for Planar Design

This paper provides an in-depth study and analysis of image processing for graphic design through chaotic encryption combined with a wavelet transform algorithm. Firstly, the traditional Mallat algorithm is optimized; since the mean value of the transform coefficients generated after the wavelet transform of the image is used as the initial value of the chaotic system to iterate, when the image is modified, then the mean value of the wavelet coefficients will also change, and the final iteration comes out as two different sequences using the property that the chaotic system is extremely sensitive to the initial conditions, so the algorithm has a certain sensitivity to tampering and localization effect. The image of the encrypted graphic design is decrypted by the chaos decryption system, and the final image information of the graphic design is obtained. In terms of the security of the graphic design image itself, the complex dynamical properties of chaos are fully utilized to encrypt it, and the algorithm has a good encryption effect after statistical characteristic analysis, attack complexity, difference analysis, adjacent pixel correlation analysis, and key sensitivity analysis. The plaintext image is decomposed in odd-even sequence using the boosting algorithm to get the sequence with an even index and the sequence with an odd index; then, the diffusion algorithm is applied to the two sequences by the prediction and update algorithm, and this process is repeated many times to get the two ciphertext sequences after scrambling, merging these two sequences, and matrixing them to get the ciphertext image. Finally, the testing of the embedded planar designed image revolves around four aspects, namely, image processing, tamper sensitivity, robustness, and imperceptibility, to examine the designed planar designed image system. In the diffusion manipulation, the key matrix for diffusion is obtained using the threshold processing method of the Local Binary Pattern (LBP) algorithm for the matrices generated by the Logistic Chaos system, which improves the randomness of the algorithm. The experimental results verify the effectiveness and security of the algorithm.

Real-time RGB image encryption for IoT applications using enhanced sequences from chaotic maps

Four chaotic maps are used herein as case study to design an embedded cryptosystem based on a pseudo-random number generator (PRNG). The randomness of the sequences is enhanced by applying the mod 1023 function and verified by analyzing bifurcation diagrams, the maximum Lyapunov exponent, and performing NIST SP 800-22 and TestU01 statistical tests. The PRNG is applied in a simple algorithm for real-time RGB images encryption on a machine-to-machine (M2M) scheme, using message queuing telemetry transport (MQTT) protocol over WiFi network and through Internet. The cryptanalysis confirms that the proposed image encryption scheme is robust to resist most of the existing attacks, such as statistical histograms, entropy, key-space, correlation of adjacent pixels, and differential attacks. The implementation of the proposed cryptosystem is done using enhanced sequences from the Logistic 1D map, and it reaches a throughput of up to 47.44 Mbit/s using a personal computer with a 2.9 GHz clock, and 10.53 Mbit/s using a Raspberry Pi 4. As a result, our proposed embedded cryptosystem is suitable to increase the security in the transmission of RGB images in real-time through WiFi networks and Internet.

SGA-Net: Self-Constructing Graph Attention Neural Network for Semantic Segmentation of Remote Sensing Images

Semantic segmentation of remote sensing images is always a critical and challenging task. Graph neural networks, which can capture global contextual representations, can exploit long-range pixel dependency, thereby improving semantic segmentation performance. In this paper, a novel self-constructing graph attention neural network is proposed for such a purpose. Firstly, ResNet50 was employed as backbone of a feature extraction network to acquire feature maps of remote sensing images. Secondly, pixel-wise dependency graphs were constructed from the feature maps of images, and a graph attention network is designed to extract the correlations of pixels of the remote sensing images. Thirdly, the channel linear attention mechanism obtained the channel dependency of images, further improving the prediction of semantic segmentation. Lastly, we conducted comprehensive experiments and found that the proposed model consistently outperformed state-of-the-art methods on two widely used remote sensing image datasets.

Characterization and correction of serial deferred charge in LSST camera ITL CCDs

Electro-optical testing and characterization of the Vera C. Rubin Observatory Legacy Survey of Space and Time Camera has shown that the focal plane charge-coupled devices developed by Imaging Technology Laboratory exhibit serial deferred charge signal in excess of the expected proportional deferred charge loss from global charge transfer inefficiency. We model the additional deferred charge as resulting from localized charge trapping in the serial register and from a signal-dependent electronic offset effect. A physically motivated forward deferred charge simulation algorithm and a reverse deferred charge correction algorithm were developed in order to correct for deferred charge during data reduction and reduce the possible impacts on Rubin Observatory project science goals. We apply the correction algorithm to calibration data and demonstrate the ability to reduce deferred charge signal measured in the overscan pixel regions and remove the serial pixel correlations resulting from the redistribution of charge between pixel neighbors during readout.

An Ultra-Lightweight and Ultra-Fast Abnormal Target Identification Network for Transmission Line

The faults caused by abnormal transmission line targets have become the main reason for the outage of transmission lines. This has brought great economic losses to society. At present, few abnormal target networks can be detected in real-time due to the restrictions of the bulky traditional model and the computing power of hardware. In order to accurately and quickly identify abnormal transmission line targets, we propose an ultra-lightweight and ultra-fast target identification network based on adaptive feature fusion in this paper, which adopts a hardware-friendly ultra-lightweight and ultra-fast backbone network to realize feature extraction. Combined with an adaptive feature fusion path aggregation network, multi-scale feature fusion is realized efficiently. The adaptive feature fusion Path Aggregation Network method includes pixel-level adaptive feature fusion and channel-level adaptive feature fusion, which can learn pixel correlation and channel importance independently. It is also beneficial to complex shape detection in complex background of a transmission line scene. In order to solve the problem of imbalance between positive and negative samples in the dataset of abnormal transmission line targets, we adopt a new loss function considering the confidence of positive and negative samples. The experiment results show that the model size is only 3.6MB, the precision (mAP) of identification for abnormal transmission line targets (4 categories) can be 61.5%, and the running time on intelligent edge identification devices is only 5 ms, with excellent performance for each indicator.

An Image Encryption Algorithm Based on the Improved Sine-Tent Map

This paper makes use of the characteristics of initial sensitivity and randomness of the chaotic map to design an image encryption algorithm based on the sine map and the tent map. The sine map is used to improve the tent map; then, the improved sine-tent map is proposed. The traditional tent map proposed in this paper has an expanded control parameter range and better chaos. In this algorithm, bit rearrangement is adopted to further improve the improved sine-tent map, which can reconstruct the output value and expand the chaotic characteristics of the map. In this algorithm, the image parameters are connected with the algorithm to generate the key. In the encryption step, a method of replacing the most significant bit and scrambling-diffusion algorithm is designed to encrypt the plaintext image. Finally, the algorithm is simulated with the experiment and evaluated with analysis; then, the experimental results are given. The evaluation results show that the ciphertext of the algorithm has high randomness, strong robustness, and better resistance to differential attacks after comparison. The correlation of the ciphertext image pixels is very low, and the algorithm is highly secure as a conclusion.

A reversible data hiding scheme for encrypted images with pixel difference encoding

In recent years, reversible data hiding of encrypted images (RDHEI) has attracted extensive attention in academia. Pixel difference is an important technique for embedding hiding information. In this paper, we propose a reversible data hiding scheme for encrypted images based on pixel prediction and pixel difference. To be specific, we first use the MED predictor to generate the predicted image, and then subtract the original image from the predicted image to obtain the difference image. Then, the histogram shifting method is used to preprocess the difference image and retain the overflow pixel value. Then, block scrambling and pixel scrambling are used to encrypt the preprocessed image. Finally, the encrypted image is embedded with pixel difference technology. The proposed scheme makes full use of the correlation of pixels and has high utilization of images. In addition, we propose a cover image selection mechanism. Experimental results show that our scheme can achieve a higher embedding rate than some of the state-of-the-art RDHEI schemes.

Generative synthesis of logos across DCT domain

Generative learning in pixel domain has achieved great success in exploiting their correlations in processing images towards desired objectives, yet learning in frequency domain could provide added benefits in exploiting pixel correlations without worrying about their spatial locations and increasing their modeling costs. In this paper, we analyze the spectral bias from a frequency perspective to overcome such limitations and hence propose a dynamic self-adaptive optimization on GAN-based generative learning, leading to a dynamic and generative logo synthesis in DCT domain. To achieve exploitation of all the pixel correlations inside the whole image regardless of their spatial locations, we introduce an approximated DCT transformation and decompose both the input images and the generated images into relatively independent DCT frequency bands. As a result, a new channel of DCT domain generative learning can be established to support the existing pixel domain learning towards improved logo synthesis. Since learning across different frequency band constantly varies, we further propose a dynamic optimization scheme to maximize the effectiveness of contributions from each individual DCT frequency band. Extensive experiments are carried out and the results in comparison with the existing state of the arts illustrate that our proposed achieves significant superiority in terms of both synthesized logo quality, integrity and variety.

Mixed reality using illumination-aware gradient mixing in surgical telepresence: enhanced multi-layer visualization

Surgical telepresence using augmented perception has been applied, but mixed reality is still being researched and is only theoretical. The aim of this work is to propose a solution to improve the visualization in the final merged video by producing globally consistent videos when the intensity of illumination in the input source and target video varies. The proposed system uses an enhanced multi-layer visualization with illumination-aware gradient mixing using Illumination Aware Video Composition algorithm. Particle Swarm Optimization Algorithm is used to find the best sample pair from foreground and background region and image pixel correlation to estimate the alpha matte. Particle Swarm Optimization algorithm helps to get the original colour and depth of the unknown pixel in the unknown region. Our results showed improved accuracy caused by reducing the Mean squared Error for selecting the best sample pair for unknown region in 10 each sample for bowel, jaw and breast. The amount of this reduction is 16.48% from the state of art system. As a result, the visibility accuracy is improved from 89.4 to 97.7% which helped to clear the hand vision even in the difference of light. Illumination effect and alpha pixel correlation improves the visualization accuracy and produces a globally consistent composition results and maintains the temporal coherency when compositing two videos with high and inverse illumination effect. In addition, this paper provides a solution for selecting the best sampling pair for the unknown region to obtain the original colour and depth.

A Multi-Image Encryption Based on Sinusoidal Coding Frequency Multiplexing and Deep Learning.

Multi-image encryption technology is a vital branch of optical encryption technology. The traditional encryption method can only encrypt a small number of images, which greatly restricts its application in practice. In this paper, a new multi-image encryption method based on sinusoidal stripe coding frequency multiplexing and deep learning is proposed to realize the encryption of a greater number of images. In the process of encryption, several images are grouped, and each image in each group is first encoded with a random matrix and then modulated with a specific sinusoidal stripe; therefore, the dominant frequency of each group of images can be separated in the Fourier frequency domain. Each group is superimposed and scrambled to generate the final ciphertext. In the process of decryption, deep learning is used to improve the quality of decrypted image and the decryption speed. Specifically, the obtained ciphertext can be sent into the trained neural network and then the plaintext image can be reconstructed directly. Experimental analysis shows that when 32 images are encrypted, the CC of the decrypted result can reach more than 0.99. The efficiency of the proposed encryption method is proved in terms of histogram analysis, adjacent pixels correlation analysis, anti-noise attack analysis and resistance to occlusion attacks analysis. The encryption method has the advantages of large amount of information, good robustness and fast decryption speed.

A novel DNA-based key scrambling technique for image encryption

Nowadays, data are the most valuable content in the world. In the age of big data, we are generating quintillions of data daily in the form of text, image, video, etc. Among them, images are highly used in daily communications. Various types of images, e.g., medical images, military images, etc. are highly confidential. But, due to data vulnerabilities, transmitting such images in a secured way is a great challenge. For this reason, researchers proposed different image cryptography algorithms. Recently, biological deoxyribonucleic acid (DNA)-based concepts are getting popular for ensuring image security as well as encryption as they show good performance. However, these DNA-based methods have some limitations, e.g., these are not dynamic and their performance results are far from ideal values. Further, these encryption methods usually involve two steps, confusion and diffusion. Confusion increases huge time complexity and needs to send one or more additional map tables with a cipher to decrypt the message. In this research, we propose a novel and efficient DNA-based key scrambling technique for image encryption that addresses the above limitations. We evaluate our proposed method using 15 different datasets and achieved superior performance scores of entropy, keyspace, cipher pixel correlations, variance of histogram, time complexity and PSNR. The experimental results show that our method can be used for image encryption with a high level of confidentiality.

Lookahead adversarial learning for near real-time semantic segmentation

Semantic segmentation is one of the most fundamental problems in computer vision with significant impact on a wide variety of applications. Adversarial learning is shown to be an effective approach for improving semantic segmentation quality by enforcing higher-level pixel correlations and structural information. However, state-of-the-art semantic segmentation models cannot be easily plugged into an adversarial setting because they are not designed to accommodate convergence and stability issues in adversarial networks. We bridge this gap by building a conditional adversarial network with a state-of-the-art segmentation model (DeepLabv3+) at its core. To battle the stability issues, we introduce a novel lookahead adversarial learning (LoAd) approach with an embedded label map aggregation module. We focus on semantic segmentation models that run fast at inference for near real-time field applications. Through extensive experimentation, we demonstrate that the proposed solution can alleviate divergence issues in an adversarial semantic segmentation setting and results in considerable performance improvements (+5% in some classes) on the baseline for three standard datasets.

Adaptively and spatially constrained dual-level trimap generation from sparse inputs

Trimap generation is currently playing an important role in the field of image matting. The current trimap generation methods cannot adaptively adjust the classification thresholds, and ignore the nonlocal correlations between pixels. Besides, they also need finely provided trimaps. This paper raises a dual-level trimap generation approach which requires sparse user inputs. The first level introduces a unary data term from adaptive learned thresholds combined with local spatial constraints to effectively overcome the drawback of fixed threshold methods. The second level introduces a smoothness term from a nonlocal affinity for foreground probability estimation, and a pixel correlation constraint is computed and added to the data term. The foreground probability is finally solved in a closed form solution and truncated to generate the final trimap. In the experiments, firstly, this approach raises a novel trimap evaluation criterion according to the two classic terms of missing and false classifications. Based on this criterion, the final trimaps generated by our approach are better than those generated by other methods. Secondly, in the final matting process from four typical matting algorithms, the matting results from the trimaps by our approach are still better than those from the trimaps by other trimap generation methods.

Image encryption scheme based on alternate quantum walks and discrete cosine transform.

As an important information medium, the digital image exists widely on the Internet. Quantum walks have the property of encrypting information. For the eneryption problem of optical digital images, an encryption scheme based on discrete cosine transform (DCT) and alternate quantum walks (AQW) is proposed in this paper. First, we use AQW and XOR operation to preprocess images in the spatial domain. Then, AQW are used to generate two random phase masks which can operate the preprocessed image and the DCT image, respectively. Finally, the encrypted image is obtained by using discrete cosine inverse exchange. The control parameters of AQW can replace the random phase mask as the key in the encryption and decryption process, so it is convenient for key management and transmission. The experimental simulation carried out the analysis of the image pixel histogram, the correlation of adjacent pixels, the robustness against noise and the sensitivity of secret keys, the results show that the image encryption method has strong security.

Visual Encryption Using Block based Scrambling Followed by Affine Encryption Technique

This article reports the Block based cipher concept followed by the affine cipher technique. The Image considered was grouped into squared (16, 32 and 64) pixel blocks then each column was shifted by specific values. These values were randomly generated prime numbers and worked as the key for scrambling. These images were investigated for their quality of scrambling using histogram and adjacent pixel correlation. The adjacent pixel correlations for 16, 32 and 64 pixel-based ciphered images were found as 0.7907, 0.7292, and 0.4783 respectively. The analysis gave the information that the level of scrambling was not satisfactory, therefore; the affine cipher technique was applied to each of the images. These images were converted into the matrix format and each element was transformed using the affine cipher. This transformed matrix is again converted inform from the image to visualize. The Histogram and adjacent pixel correction for these images were much improved.

CoSTA: unsupervised convolutional neural network learning for spatial transcriptomics analysis

BackgroundThe rise of spatial transcriptomics technologies is leading to new insights about how gene regulation happens in a spatial context. Determining which genes are expressed in similar spatial patterns can reveal gene regulatory relationships across cell types in a tissue. However, many current analysis methods do not take full advantage of the spatial organization of the data, instead treating pixels as independent features. Here, we present CoSTA: a novel approach to learn spatial similarities between gene expression matrices via convolutional neural network (ConvNet) clustering.ResultsBy analyzing simulated and previously published spatial transcriptomics data, we demonstrate that CoSTA learns spatial relationships between genes in a way that emphasizes broader spatial patterns rather than pixel-level correlation. CoSTA provides a quantitative measure of expression pattern similarity between each pair of genes rather than only classifying genes into categories. We find that CoSTA identifies narrower, but biologically relevant, sets of significantly related genes as compared to other approaches.ConclusionsThe deep learning CoSTA approach provides a different angle to spatial transcriptomics analysis by focusing on the shape of expression patterns, using more information about the positions of neighboring pixels than would an overlap or pixel correlation approach. CoSTA can be applied to any spatial transcriptomics data represented in matrix form and may have future applications to datasets such as histology in which images of different genes are from similar but not identical biological sections.

Improved fuzzy clustering for image segmentation based on a low-rank prior

Image segmentation is a basic problem in medical image analysis and useful for disease diagnosis. However, the complexity of medical images makes image segmentation difficult. In recent decades, fuzzy clustering algorithms have been preferred due to their simplicity and efficiency. However, they are sensitive to noise. To solve this problem, many algorithms using non-local information have been proposed, which perform well but are inefficient. This paper proposes an improved fuzzy clustering algorithm utilizing nonlocal self-similarity and a low-rank prior for image segmentation. Firstly, cluster centers are initialized based on peak detection. Then, a pixel correlation model between corresponding pixels is constructed, and similar pixel sets are retrieved. To improve efficiency and robustness, the proposed algorithm uses a novel objective function combining non-local information and a low-rank prior. Experiments on synthetic images and medical images illustrate that the algorithm can improve efficiency greatly while achieving satisfactory results.

The Earth Environment Change Detection Method Based on the Variable Weight Markov Random Field Combined with the Space Gravity Model

For the traditional earth environment change detection algorithm based on remote sensing image, only the band information of the image is utilized, and it is difficult to obtain the defect of the complete change detection result. Based on the spectral information of the image, the change detection method based on Markov random field model introduces the spatial correlation of pixels, which improves the accuracy of change detection to some extent. However, due to the over-use of spatial correlation in the process of introducing pixel spatial information, these methods have the disadvantage of poor detection results due to the fixed weight parameters in the modelling process. Aiming at the limitations of these methods, this paper proposes the environment of earth change detection method based on the variable weight markov random field combined with the space gravity model. Using the space gravitation model, the use of pixel spatial information is more reasonable, and the idea of variable weight is introduced to improve the defect that the change detection result is too smooth due to the fixed weight parameter. Experiments on real high-resolution remote sensing image datasets verify the effectiveness and feasibility of the proposed method.

A secure image cryptosystem via multiple chaotic maps

This work focused on introducing a new two-dimensional (2D) chaotic system. It is combined of some existing maps, the logistic, iterative chaotic map with infinite collapse, and Henon maps; we called it 2D-LCHM. The assessment of the actual performance of 2D-LCHM presents its sensitivity to a tiny change in the initial condition. Besides, its dynamics behavior is very complicated. It also has hyperchaotic properties and good ergodicity. The proposed system is occupied with designing a new image encryption system. Changing the image pixels’ locations is the primary step in the encryption process. The original image is splitting into four blocks to create four different keys based on 2D-LCHM; this reduces the computation time and increases the complexity. To obtain the encryption image, we have to repeat the partitioning process several times for each block. The encryption image’s efficiency is shown through some performance analysis such as; image histogram, the number of pixels changes rate (NPCR), the unified average changing intensity (UACI), pixels correlation, and entropy. The proposed system is compared with some efficient encryption algorithms in terms of chaocity attributes and image performance; the analysis result showed consistent improvement.