Correlation Of Pixels Research Articles

Efficient image segmentation and implementation of K-means clustering

The image segmentation scheme is proposed in this research article. It offers the determined the certain images and grouping of images from colleague frame. In this research, the K-Means algorithm is utilized for image segmentation. The images have transformed into Gray scale images then it facilitates the extraction of clusters. The K-Means technique is based on groupings of similar pixels and allocation of the median. Repeating the same process several times provides better object identifications. This algorithm will provide you good quality output images. Object discrimination relies on the correlation of available pixels in the image. After being segmented, the image was further processed for improved visibility.

Vegetation variation trend and its influencing factors in Urad Grassland over remote sensing

In this work, the temporal-spatial dynamic variation of vegetation coverage from 2010 to 2019 in Urad Grassland has been explored by remote sensing technique. The change of precipitation and temperature has obvious effects on the vegetation, which affects the agriculture and the stability of the ecosystem. Pixel dichotomy approach and correlation analysis are introduced to analyse the temporal and spatial evolution trend of vegetation coverage and its response to climate change in the past 10 years. Specifically, the correlation between vegetation coverage and the critical climate impacting factors such as temperature and precipitation are fully investigated. The results show the vegetation coverage in the study area was influenced by effectors including climate, topography, human activities and government policies. The average annual vegetation coverage showed a downward trend in general from 2010 to 2019. Statistical correlation analysis indicates that the correlation between the vegetation coverage and the precipitation is positive in most parts of the area, while the correlation between the vegetation coverage and the precipitation shows negative with difference with various geographic features. As a result, precipitation was the major natural factor that affected dynamics of vegetation coverage in Urad grassland.

A Novel Image Encryption Algorithm Based on Bernoulli Shift Map

This paper puts forward a chaotic encryption algorithm based on Bernoulli Shift Map. This algorithm adopts dynamic encryption key and uses plaintext image parameters to control generation of chaotic key, and enhance connection between chaotic key and plaintext data; the iterative operation precision of chaotic map during encryption is high, avoiding cycle of chaotic system iteration process. The NPCR and UACI analysis, adjacent pixels correlation analysis and image information entropy analysis on encrypted image show this encryption algorithm has favorable encryption characteristics such as larger space for key, sensitivity to plaintext and random distribution of ciphertext.

Efficient High-Resolution Video Compression Scheme Using Background and Foreground Layers

Video coding using dynamic background frame achieves better compression compared to the traditional techniques by encoding background and foreground separately. This process reduces coding bits for the overall frame significantly; however, encoding background still requires many bits that can be compressed further for achieving better coding efficiency. The cuboid coding framework has been proven to be one of the most effective methods of image compression which exploits homogeneous pixel correlation within a frame and has better alignment with object boundary compared to traditional block-based coding. In a video sequence, the cuboid-based frame partitioning varies with the changes of the foreground. However, since the background remains static for a group of pictures, the cuboid coding exploits better spatial pixel homogeneity. In this work, the impact of cuboid coding on the background frame for high-resolution videos (Ultra-High-Definition (UHD) and 360-degree videos) is investigated using the multilayer framework of SHVC. After the cuboid partitioning, the method of coarse frame generation has been improved with a novel idea by keeping human-visual sensitive information. Unlike the traditional SHVC scheme, in the proposed method, cuboid coded background and the foreground are encoded in separate layers in an implicit manner. Simulation results show that the proposed video coding method achieves an average BD-Rate reduction of 26.69% and BD-PSNR gain of 1.51 dB against SHVC with significant encoding time reduction for both UHD and 360 videos. It also achieves an average of 13.88% BD-Rate reduction and 0.78 dB BD-PSNR gain compared to the existing relevant method proposed by X. HoangVan [18].

Color image encryption algorithm based on DNA code and alternating quantum random walk

In recent years, image encryption technology has attracted much attention. As people pay more attention to communication privacy and network security, the requirements for information encryption technology are more stringent. As one of the information carriers, images are valuable for carrying the effectiveness and vividness of the information. This paper proposes a color image encryption algorithm based on DNA encoding and alternating quantum random walk. Quantum random walk is an excellent cryptographic tool that participates in all parts of the algorithm process, and DNA encoding is used as the core encryption method to complete the algorithm. This article describes the encryption and decryption process in detail, and conducts simulation experiments to verify and analyze the results of the proposed algorithm. In the simulation stage, we design the simulation key parameters, encode the color image encryption and decryption experiments, and carry out related analysis. The experimental results show that the color image encryption algorithm proposed in this paper can perform safe and effective color image encryption. The correlation analysis shows that the image histogram after encryption is stable, the pixel correlation coefficient approaches 0, and the key space is <inline-formula><tex-math id="M2">\begin{document}${2^{ 128 }} $\end{document}</tex-math><alternatives><graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="23-20211255_M2.jpg"/><graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="23-20211255_M2.png"/></alternatives></inline-formula>, the three-channel information entropy reaches more than 7.997, which can resist statistical attacks, brute force attacks and other attack methods. In addition, DNA coding has unique biological characteristics in addition to the novel coding and calculation methods, which provide new ideas and directions for cryptographic research.

Color image encryption using linear feedback shift registers by three dimensional permutation and substitution operations

This study proposes a scale-invariant digital color image encryption method that includes three main steps the pre-substitution, the 3D scale-invariant modular chaotic map, and the post-substitution. 1) The pre-substitution: At the first stage, pixels of plain sub-images are XOR with different key patterns. By starting from one of the plain sub-images, the pixels of the selected plain sub-image is XOR with the initial key, and the result is used as the cipher sub-image and as the next key pattern for performing XOR operations on the next plain sub-image. In other words, the XOR result of each step is used as the next step key pattern, 2) the 3D permutation: At the second stage, first the red, green, and blue components of a $M times N$ color image is divided to m sub-images with size $n times n$. Then m sub-images are partitioned into $k = leftlceil {frac{m}{n}} rightrceil $ windows $W_1$ to $W_k$ with size $n times n$ sub-images. The last two i؟¼$W_k-1$ and i؟¼$W_k$ windows may be overlap in several sub-images. Finally, the three-dimensional modular chaotic maps are performed on the $W_1$i؟¼ to $W_k$i؟¼ windows with MIPF keys and selected by LFSR, 3) the post-substitution: At the final stage, the $M times N$ color image is initially divided into a set of color sub-images. Then, the 24-bit pixels of each sub-images are circularly shifted with several bits specified in the secret key. Modular arithmetic is used in the 3D scale-invariant chaotic maps to increase keyspace and enhance security parameters. With repeat at least one round of main steps, the proposed encryption scheme reaches optimum parameter values and it is highly sensitive to minor differences in both secret key and plain image. The proposed encryption method for images improves the standard parameters of evaluation such as entropy, adjacent pixel correlations, histogram, and expanded keyspace.

New DNA Coded Fuzzy Based (DNAFZ) S-Boxes: Application to Robust Image Encryption Using Hyper Chaotic Maps

This article proposes a novel approach of improvising the cryptographic features of substitution-boxes (S-Box) based on the Choquet Fuzzy Integral (CFI) and DNA techniques. First, we propose a strong structure for the construction of four S-Boxes using CFI. The key for generating the CFI based (FZ) S-Boxes consists of two parts, namely, an external secret key and a secret image. Each of these FZ S-boxes is then encoded using DNA techniques, with dynamic rules selection which is dictated by a secret control code. The resultant four S-boxes are designated as DNAFZ S-Boxes. To apply for image encryption, the plain image is, at first 8-bit binary-coded, shuffled by an M-sequence, and down-sampled into four sub-images. Subsequently, the pixel values of each sub-image are replaced with the corresponding values of one of the four DNAFZ S-Boxes. Next, each DNAFZ encoded sub-image is diffused with a different DNA encoded chaotic sequence from Chen’s hyper-chaotic map. Finally, the four DNAFZ/Chaotic encoded sub-images are combined to build the final encrypted image. The proposed DNAFZ S-boxes shows excellent statistical properties under majority logic criterions such as correlation, homogeneity, energy, entropy, and contrast. Moreover, numerical simulation is used to examine the efficacy of encrypted images against different attacks. In particular, the values of the pixel correlation coefficient are found to be quite small either horizontally, vertically, or diagonally (between 7.8597e-04 and 0.00527, between 8.7856e-04 and 0.00452, and between 0.00241 and 0.00021, respectively). In addition, the information entropy of the encrypted image is found to be within the range of (7.9965:7.9989) which is very near to the ideal value of 8. As for the UACI and the NPCR, they are in the ranges between 33.46 and 33.32 and between 99.58 and 99.62, respectively. These values are also very close to the optimum ones. The results are compared to those of other encryption algorithms and proved that the proposed encryption method delivers better results than other conventional ones including LSS chaotic map, Arnold transforms, Dynamic Henon map, Hybrid chaotic map optimized substitution, and cubic S-Box.

Chaotic Image Encryption Algorithm Based on Zigzag Transform With Bidirectional Crossover From Random Position

This paper proposes a Zigzag transform that starts at random positions and crosses bidirectionally (ZZBCRP) to scramble the image. The number of sequence types of this new Zigzag transform is the number of pixels in the plaintext image, which is more and more complicated than other Zigzag transform methods, and Arrangement in a bidirectional crossover method can better disrupt the pixel correlation of the plaintext image. After multiple rounds of Zigzag transformation, the Logistic system is used to generate a sorted sequence to scramble the image. Then the chaotic sequence generated by the hyperchaotic Chen system is used. Perform a reversible diffusion operation on the image, and finally analyze the security of the algorithm.

Reversible Data Hiding Based on Adaptive Multiple Histograms Modification

Pixel value ordering prediction has been verified as an effective mechanism to exploit image redundancy for reversible data hiding (RDH) and numerous extensions have been devised. However, their performance is still unsatisfactory since the error modification is generally fixed and independent of image content. In this paper, a new RDH scheme is proposed by incorporating pixel distance to realize adaptive multiple histograms modification (AMHM). During exploiting the correlation between the largest/smallest pixel and any other one in the scope of pixel block, we propose to process every two correlated pixels successively following the ascending order of their distance. Specifically, the generated errors with a given distance are collected and verified. If they are all shiftable errors, the follow-up errors would be collected into the next sub-histogram. In this way, a histogram sequence is adaptively generated such that different modification mechanisms can be taken for different sub-histograms to achieve adaptive embedding. Finally, AMHM for conventional prediction-error expansion (PEE) and AMHM for 2D PEE have been both realized in this paper. Experimental results show that AMHM is of great significance to better exploit pixel correlation and the proposed scheme outperforms a series of the latest schemes.

Scale Invariant Digital Color Image Encryption Using a 3D Modular Chaotic Map

Cryptography is one of the most important security mechanisms for the transmission of digital media on the Internet. Most proposed cryptographic image methods based on chaotic maps are dependent upon image sizes and most of them worked on square images. In this paper for tackling this problem, a scale-invariant color image encryption method in three-dimensional space is presented. At first, the two-dimensional color image is converted into three-dimensional space, in this case, the red, green, and blue color spectrums are divided into a set of gray-level square sub-images. Then, to have confusion and diffusion properties, the 3D substitution and 3D permutation operation are performed on the sub-images. In substitution operations, the pixel values of the sub-images are changed with the help of XOR and circular shift operators with appropriate keys. In permutation operation, the position of the pixels is changed using modular three-dimensional chaos mappings. To have scale-invariant three-dimensional permutation, the sub-images are divided into one or more windows with equal size, and then 3D modular chaotic map operations are performed on each window with separate keys. Depending on the number of sub-images, there may be two last windows that have overlapping. To increase the speed of color image encryption, the steps of a 3D modular chaotic map on the windows can be implemented in parallel. The proposed approach relative to the similar color image encryption algorithms increases the key space and improves standard parameters, such as entropy, sensitivity, adjacent pixel correlations, and histogram uniformity.

Non-imaging target recognition algorithm based on projection matrix and image Euclidean distance by computational ghost imaging

With the development of target recognition technology, people pay more and more attention to target recognition. This paper proposed a non-imaging target recognition technology based on projection matrix and image Euclidean distance by computational ghost imaging. The main features of spatial sample images are extracted by principal component analysis. The projection matrix of eigenspace is used as the modulation matrix in computational ghost imaging algorithm to reduce data redundancy. And by calculating the image Euclidean distance, the correlation of pixels is taken into full consideration in spatial position, so as to improve the accuracy of traditional target recognition algorithms. In this paper, the target recognition is realized by directly using the detector observations of the computational ghost imaging system based on the eigenspace projection matrix and the calculation of the image Euclidean distance. The algorithm not only has better recognition performance without reconstructing the image.

Chaotic Deep Network for Mobile D2D Communication

Device-to-Device (D2D) communication has now become one of the most promising technologies in wireless communications of the Internet of Things (IoT). In view of the requirements on data security, response speed, signal decryption quality and storage cost of mobile devices in D2D networks, we propose chaotic deep network (CDN) to achieve secure transmission which is swifter, higher quality and lower cost. The proposed scheme consists of a prepositive nonrepetitive training procedure, a well-designed parallel encryption process, and a set of pretrained chaotic deep neural decryption networks. Benefiting from the utilization of deep learning methods, CDN achieves the swift and accurate decryption at a much lower sampling rate, which brings huge dimension reduction of both measurement matrices and ciphertext signals. Also, chaotic initial values and parameters are applied to matrix generation and network training, leading to great time reduction, storage saving and security improvement. In addition, CDN incorporates the frameworks of semitensor product (STP) and block-based image processing (BIP), which not only breaks through the dimension matching limitation of matrix multiplication by using STP but also maintains the parallelizable block-cipher mode of BIP. Proved by experiments, CDN at the sampling ratio of 25% achieves almost the same or even better peak signal to noise ratio compared to other 8 most frequently used methods at that of 50% for the same test images, and obtains better visual effects. When using a large-sized image of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2^{10}\times 2^{10}$ </tex-math></inline-formula> pixels for the experiments, CDN is dozens and even hundreds of times faster than those methods, while reduces the size of measurement matrix from a 500-kB level to a 3-kB level, and the size of compressed data to be transmitted can be reduced by more than 50% as well. Besides, the total key space is approximately <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$10^{93}$ </tex-math></inline-formula> . The adjacent pixel correlation is less than 0.01.

Hyperspectral Anomaly Detection via Graph Dictionary-Based Low Rank Decomposition with Texture Feature Extraction

The accuracy of anomaly detection in hyperspectral images (HSIs) faces great challenges due to the high dimensionality, redundancy of data, and correlation of spectral bands. In this paper, to further improve the detection accuracy, we propose a novel anomaly detection method based on texture feature extraction and a graph dictionary-based low rank decomposition (LRD). First, instead of using traditional clustering methods for the dictionary, the proposed method employs the graph theory and designs a graph Laplacian matrix-based dictionary for LRD. The robust information of the background matrix in the LRD model is retained, and both the low rank matrix and the sparse matrix are well separated while preserving the correlation of background pixels. To further improve the detection performance, we explore and extract texture features from HSIs and integrate with the low-rank model to obtain the sparse components by decomposition. The detection results from feature maps are generated in order to suppress background components similar to anomalies in the sparse matrix and increase the strength of real anomalies. Experiments were run on one synthetic dataset and three real datasets to evaluate the performance. The results show that the performance of the proposed method yields competitive results in terms of average area under the curve (AUC) for receiver operating characteristic (ROC), i.e., 0.9845, 0.9962, 0.9699, and 0.9900 for different datasets, respectively. Compared with seven other state-of-the-art algorithms, our method yielded the highest average AUC for ROC in all datasets.

Three [formula omitted]-secret image sharing schemes based on homogeneous linear recursion

In this paper, three novel secret image sharing schemes based on homogeneous linear recursion are introduced. Simple sharing, various methods for the recovery, small shadow image size and high resolution are some of the merits of our schemes. Moreover, shadow images generated in schemes 2 and 3 are unrevealing and are secured against attacks based on correlation of adjacent pixels, without any need to perform extra computations such as permutation or encryption, which usually were used in the previous schemes.

A new block cipher for image encryption based on multi chaotic systems

In this paper, a new algorithm for image encryption is proposed based on three chaotic systems which are Chen system,logistic map and two-dimensional (2D) Arnold cat map. First, a permutation scheme is applied to the image, and then shuffled image is partitioned into blocks of pixels. For each block, Chen system is employed for confusion and then logistic map is employed for generating subsititution-box (S-box) to substitute image blocks. The S-box is dynamic, where it is shuffled for each image block using permutation operation. Then, 2D Arnold cat map is used for providing diffusion, after that XORing the result using Chen system to obtain the encrypted image.The high security of proposed algorithm is experimented using histograms, unified average changing intensity (UACI), number of pixels change rate (NPCR), entropy, correlation and keyspace analyses.

Medical Image Segmentation Based on Combination Method

This paper studies the data segmentation of medical images based on compressed sensing. We do not use image data as interpolation data, but as constraints to construct surface patches with quadratic polynomial approximation accuracy, study the properties and correlation of pixels, and take these edge points as constraints, and map the discrete image data into continuous mathematical functions, so that the continuous function or point data not only has the shape recommended by the image data, but also has a higher segmentation accuracy, and then realize the fine segmentation of images, which provides a new research idea to solve the difficult problems of spatial data representation, and has good theoretical significance and application value.

Evolvable Image Filter based on Rotation Invariant Pixel Correlations

As one of the most typical nonlinear filters, weighted median filter has considerable filter behaviour by assigning weights. But each pixel has only one weight in the traditional weighted median filter. Correlations between pixel and other pixels under the filtering template are not considered. A novel rotation-invariant image filter model is proposed here which has a stable filtering performance when filtering on rotated and reversed images, symmetry of the correlations is utilized to reduce the complexity. Cartesian Genetic Programming is adopted to evolve image filter operators. Experiments about rotated images show that this rotation invariant model exhibits robust characteristics. Image denoising of different images show that it has better properties of edge or details preservation, efficient noise attenuation.

New Image Encryption Based on Pixel Mixing and Generating Chaos System

There was suggested new utilizing of the dispersion principle, based on the Lorenz Map, and chaos theory is known as perfect for using cryptography applications for their properties like unpredictability and sensitive initial states as one of the complex encryption approaches. to disengage the color of pixels (R, G, B). Was created this suggest with mixing all each color pixel to swap horizontally, vertically, or diagonally, finally using XOR operation. only milliseconds to encryption and decryption image, because the behavior of block cipher that leads to the period for the image to be decrypted fairly close as the images. The experiments demonstrate that the information security capabilities will be both safe more efficient, the result of our analysis of picture quality evaluation, differential attacks (NPSR), (UACI), Pixels correlation shows the quality and strength of encryption processing, Entropy show good results and closely to 8.

Empirical Analysis of Robust Chaotic Maps for Image Encryption

The rate of transferring data in the form of text, image, video or audio over an open network has drastically increased. As these are carried out in highly sophisticated fields of medicine, military and banking, security becomes important. In- order to enhance security for transmission, encryption algorithms play a vital role. So as to enhance the proficiency of the existing encryption methods and for stronger anti attack abilities, chaotic based cryptography is essential. Chaotic based encryption has advantages of being sensitive to initial conditions and control parameters. Images have features like bulk data capacity and high inter pixel correlation. Transmission of such medical data should be highly confidential, integral and authorized. Hence chaos-based image encryption is an efficient way of fast and high-quality image encryption. It has features like good speed, complexity, highly secure, reasonable computational overhead and power. In this paper a comprehensive analysis and an evaluation regarding the capabilities of different discrete time domain chaotic maps were carried out on a proposed image encryption method. The experimental results show high efficiency for the proposed image encryption technique.

Improved Point–Line Visual–Inertial Odometry System Using Helmert Variance Component Estimation

Mobile platform visual image sequence inevitably has large areas with various types of weak textures, which affect the acquisition of accurate pose in the subsequent platform moving process. The visual–inertial odometry (VIO) with point features and line features as visual information shows a good performance in weak texture environments, which can solve these problems to a certain extent. However, the extraction and matching of line features are time consuming, and reasonable weights between the point and line features are hard to estimate, which makes it difficult to accurately track the pose of the platform in real time. In order to overcome the deficiency, an improved effective point–line visual–inertial odometry system is proposed in this paper, which makes use of geometric information of line features and combines with pixel correlation coefficient to match the line features. Furthermore, this system uses the Helmert variance component estimation method to adjust weights between point features and line features. Comprehensive experimental results on the two datasets of EuRoc MAV and PennCOSYVIO demonstrate that the point–line visual–inertial odometry system developed in this paper achieved significant improvements in both localization accuracy and efficiency compared with several state-of-the-art VIO systems.