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Spectral clustering, as an algorithm based on graph theory and spectral theory, has shown excellent performance in the classification tasks of hyperspectral images in recent years. Although better results have been achieved, some challenges still exist. The inclusion of a priori information can increase the performance of spectral clustering algorithms; however, in practice, it is often unable to meet the demand for a priori information; at the same time, spectral clustering for the quality of the similarity matrix is demanding, and some of the current algorithms are to improve the similarity matrix from the aspect of data planning, and ultimately for the label feature matrices are diluted. In response to the above problems, this paper proposes a self-supervised spectral clustering with spectral embedding (SESSC). The algorithm obtains a low-dimensional representation of the data through spectral embedding, which can simplify clustering while preserving feature information; then uses the similarity matrix about the data and the guidance of the sample constraint information to obtain a new similarity matrix in order to further refine the structural graph, and the result can feed back and optimize the label feature matrix and the low-dimensional representation of the data. Additionally, we introduce a fractional theory in the update of the sample variable matrix, which assures the integrity and validity of the information in the update. Experimental results show that the proposed algorithm has better performance in hyperspectral image classification than existing spectral clustering algorithms. [ABSTRACT FROM AUTHOR]

Interval type-2 fuzzy sets not only have stronger ability to deal with uncertainty, but also have low computational complexity than general type-2 fuzzy set, so they are widely used in fuzzy clustering methods. However, most existing interval type-2 fuzzy clustering methods are still sensitive to noise and lack a certain degree of robustness in segmenting images with noise. Therefore, this paper proposes a novel interval type-2 enhanced kernel possibilistic fuzzy local and non-local information c-means clustering method for segmenting images with high noise. Interval type-2 possibilistic fuzzy clustering with Lambert-W function is first extended to obtain a novel interval type-2 enhanced possibilistic fuzzy clustering with product partition. Then deep local neighborhood information including local and non-local information is used to constrain interval type-2 enhanced possibilistic fuzzy product partition c-means clustering, and a robust interval type-2 enhanced possibilistic fuzzy deep local information clustering with kernel metric is proposed. Experimental results demonstrate that the proposed algorithm significantly outperforms the latest fuzzy clustering-related algorithms in the presence of high noise. [ABSTRACT FROM AUTHOR]

Local neighborhood information plays an important role in robust fuzzy clustering-related segmentation algorithms, and how to construct local information items is the key to robust fuzzy clustering. Based on existing local information constraints, this paper proposes a model to describe the hierarchy relationship of local neighborhood windows in the master–slave neighborhood model, which combines spatial distance with gray information to suppress the influence of noise on current pixel clustering, and can also well control the balance of noise suppression and detail preservation. Based on this model, this paper proposes a robust fuzzy clustering segmentation algorithm with master-slave neighborhood information constraints. When constraining the neighborhood pixels of a pixel (that is the master neighborhood pixels), the algorithm will further constrain the pixels in the neighborhood window around the master neighborhood pixel (that is the salve neighborhood pixels), thus enhancing the robustness of the algorithm. Experiments results show that the proposed algorithm has good segmentation performance and strong anti-noise performance, even significantly outperforms existing state-of-the-art robust fuzzy clustering-related algorithms in the presence of high noise. [ABSTRACT FROM AUTHOR]

To improve the segmentation performance and anti-noise robustness of existing weighted kernel intuitionistic fuzzy clustering, a robust kernelized total Bregman divergence-based fuzzy local information clustering motivated by intuitionistic fuzzy information is proposed. In this algorithm, a kernelized total Bregman divergence is extended by a polynomial kernel function, and the corresponding intuitionistic kernelized total Bregman divergence is put forward to measure the difference between intuitionistic fuzzy sets. Then, the weighted local information is introduced into the objective function of intuitionistic fuzzy clustering, and the similarity between the current pixel and its neighborhood pixels is constructed to better describe the influence of neighborhood pixels on the current pixel. Finally, the square root of the deviation between the current pixel and the mean value of its neighboring pixels is used to adjust the local spatial information to improve the robustness to noise or outliers and further enhance the anti-noise ability of the algorithm. Experimental results show that the proposed algorithm has better clustering performance and stronger anti-noise robustness than existing state-of-the-art fuzzy clustering-related algorithms. [ABSTRACT FROM AUTHOR]

Aiming at the shortcoming of existing robust intuitionistic fuzzy clustering and its variant algorithms in the presence of high noise, we will explore a novel intuitionistic fuzzy clustering-related segmentation algorithm with strong robustness. To enhance the anti-noise robustness and segmentation accuracy of intuitionistic fuzzy clustering-related algorithms, an intuitionistic fuzzy information-driven total Bregman divergence fuzzy clustering with multiple local information constraints is proposed in this paper. In this algorithm, a modified total Bregman divergence with spatial information constraints is firstly constructed to replace existing squared Euclidean distance in intuitionistic fuzzy clustering, which can further enhance the ability of noise suppression. By introducing an intuitionistic fuzzy weighted local information into the objective function of intuitionistic fuzzy c-means clustering, its noise sensitivity can be reduced and image details can be preserved effectively. Combining neighborhood spatial information, neighborhood gray information with the normalized variance of neighborhood pixels, a local similarity measure between the current pixel and its neighborhood pixels is constructed to better describe the influence degree of neighborhood pixels on the current pixel, and it is adaptively embedded into modified total Bregman divergence-based intuitionistic fuzzy clustering. In the end, a robust total Bregman divergence-based fuzzy weighted local information clustering algorithm motivated by intuitionistic fuzzy information of the image is obtained to solve the problem of robust image segmentation, and its convergence is strictly proved by the Zangwill theorem. Many experimental results show that the segmentation accuracy ACC of the proposed algorithm can be as high as more than 90%, and the misclassification rate ME can be as low as less than 28%. Therefore, the proposed algorithm has better segmentation performance and robustness than the existing state-of-the-art intuitionistic fuzzy clustering-related segmentation algorithm in the presence of noise. [ABSTRACT FROM AUTHOR]

Hyper‐spectral image (HSI) clustering has become a hot spot in remote sensing image research. However, due to the large amount and high dimension of HSI data, some traditional image clustering methods are no longer suitable, and even lead to poor clustering performance and long computing time. Therefore, this paper proposes a novel clustering method for HSI to make up for the shortcomings of traditional clustering methods. Firstly, a fuzzy similarity matrix is constructed by using the bipartite graph to obtain low‐dimensional hyper‐spectral data, which can reduce the complexity and the operation time of the algorithm. Secondly, fuzzy membership mapping is carried out through the constructed bipartite graph, and a non‐negative regularization term is added to limit ill‐conditioned problems. Thirdly, this paper uses the Geman‐McClure function to optimize Euclidean distance. Finally, in this paper, the spectral information of HSI is considered while the neighbourhood information of HSI is added to achieve the unity of space spectrum, which enhances the robustness of the algorithm and improves the clustering performance of the algorithm. Compared with existing HSI clustering algorithms, the proposed algorithm has higher clustering accuracy and efficiency. [ABSTRACT FROM AUTHOR]

• A joint learning model for superpixel-based fuzzy sparse subspace clustering is established. • A four-level alteration iterative algorithm for superpixel-based image segmentation is proposed. • A centroid shift strategy suitable for image contents is used to generate superpixels. • Experimental results indicate that the proposed algorithm has very good performance. Sparse subspace clustering (SSC) is an important image segmentation method that constructs a self-representation coefficient matrix to represent the relationships between pixels, and then uses spectral clustering to achieve clustering. Compared with other unsupervised segmentation algorithms, SSC has good segmentation performance. However, SSC has a high computational complexity when processing large-sized image. To improve computational efficiency, many researchers have proposed superpixel-based SSC algorithms, which process superpixels instead of pixels and improve efficiency through preprocessing. Due to the sensitivity of superpixel generation to noise, superpixel-based SSC algorithms still have poor robustness. Additionally, preprocessing increases the complexity of the algorithm. To address these issues, this paper proposes a robust superpixel-based fuzzy sparse subspace clustering algorithm. This algorithm combines fuzzy sparse subspace clustering with superpixel generation, and it constructs a unified optimization learning framework through fuzzy C-multiple-means clustering to improve segmentation performance and reduce complexity. Additionally, this paper introduces additional features of superpixel in sparse subspace clustering to further enhance the segmentation performance of the algorithm. Experiment results indicate that the proposed algorithm not only outperforms existing state-of-the-art robust segmentation algorithms independent of superpixels, but also is superior to the latest superpixel-based segmentation algorithms. [ABSTRACT FROM AUTHOR]

With the increase in sample number or image size, it is very important to improve the efficiency of picture fuzzy clustering algorithm in data classification or image segmentation. This paper presents an algorithm to improve the convergence speed of picture fuzzy clustering for large-scale data. According to the existing suppression fuzzy clustering idea, the maximum value of picture fuzzy partition information of picture fuzzy clustering is further increased and other values are decreased. The modified picture fuzzy partition information is used to update the cluster center of the sample, which speeds up the convergence speed of the algorithm. The adaptive selection of suppression factors makes the picture fuzzy clustering obtain faster operation efficiency and better segmentation performance. In order to further improve the performance of picture fuzzy clustering algorithm for noise image segmentation, an adaptive robust suppression picture fuzzy clustering algorithm with neighborhood spatial information constraints is proposed. Experimental results show that the proposed algorithm not only shortens the operation speed of existing picture fuzzy clustering, but also improves the robustness to noise. [ABSTRACT FROM AUTHOR]

In recent years, several superpixel-segmentation methods have been developed to efficiently segment noisy images. However, these methods still face challenges such as high computational complexity and poor adaptability. Therefore, this paper develops a novel superpixel-based robust segmentation model including two modules: superpixel generation and superpixel-based image segmentation. In the superpixel generation module, a fuzzy factor containing local spatial information of pixels is introduced into fuzzy possibilistic clustering algorithm with local search. In the superpixel-based segmentation module, a superpixel-based fuzzy C-means algorithm with local spatial information of superpixels is proposed, which nonlinearly combines the membership of superpixels with the membership of their neighboring superpixels. The experimental results demonstrate that the proposed method outperforms existing state-of-the-art segmentation algorithms in terms of evaluation indexes and visual effects. [ABSTRACT FROM AUTHOR]

To improve the anti-noise ability of credibilistic intuitionistic fuzzy c-means clustering method (CIFCM) for image segmentation, this paper proposes a robust credibilistic intuitionistic fuzzy c-means clustering method based on credibility of pixels and intuitionistic fuzzy entropy. Firstly, a new similarity measure is constructed by utilizing the grayscale and spatial relationship between the current pixel and its neighborhood pixels. Secondly, it is embedded into the objective function of credibilistic intuitionistic fuzzy c-means clustering, and a new robust clustering method with spatial constraints is presented to effectively solve the segmentation problem of image corrupted by high noise. In the end, the convergence of this proposed robust clustering method is strictly proved by iterated convergence theorem. Experimental results show that proposed algorithm has better noise-suppression ability and more satisfactory segmentation results than CIFCM algorithm. [ABSTRACT FROM AUTHOR]

To improve the effectiveness and robustness of the existing semisupervised fuzzy clustering for segmenting image corrupted by noise, a kernel space semisupervised fuzzy C-means clustering segmentation algorithm combining utilizing neighborhood spatial gray information with fuzzy membership information is proposed in this paper. The mean intensity information of neighborhood window is embedded into the objective function of the existing semisupervised fuzzy C-means clustering, and the Lagrange multiplier method is used to obtain its iterative expression corresponding to the iterative solution of the optimization problem. Meanwhile, the local Gaussian kernel function is used to map the pixel samples from the Euclidean space to the high-dimensional feature space so that the cluster adaptability to different types of image segmentation is enhanced. Experiment results performed on different types of noisy images indicate that the proposed segmentation algorithm can achieve better segmentation performance than the existing typical robust fuzzy clustering algorithms and significantly enhance the antinoise performance. [ABSTRACT FROM AUTHOR]

Focusing on the currently available multi-view fuzzy clustering algorithms, many of which frequently lack robustness and are hence less frequently used in image segmentation. We present a multi-view fuzzy clustering image segmentation algorithm in this research, along with an autonomous view-weight learning mechanism. Firstly, to ensure that each view has the best view weight, the algorithm adds a view weight factor. Secondly, it introduces the weighted fuzzy factor and the kernel distance metric, the role of the weighted fuzzy factor is to collect the local spatial information and local grey scale information to preserve as much of the image’s detailed information as feasible during segmentation. The role of the kernel distance metric is to lessen the influence of outliers and noisy points on image segmentation. Finally, the technique for resolving the issue of image uncertainty and fuzzy factor selection introduces the concept of interval type-2 fuzzy c-means clustering. Numerous experiments on different images demonstrate that the proposed algorithm in this paper is more robust than previous multi-view fuzzy clustering algorithms for solving noise image segmentation problems. It is also more effective at segmenting images contaminated by noise and can better retain the detailed information in the image. [ABSTRACT FROM AUTHOR]

• A novel total-aware suppressed possibilistic clustering algorithm is proposed. • The convergence of the proposed algorithm is strictly proved by Zangwill theorem. • Robust total suppressed possibilistic clustering for text data or images with noise is obtained. • Experimental results show that all total suppressed algorithms outperform CPCM and SPCM. Cutset-type possibilistic c-means (CPCM) is an improved version of possibilistic c-means (PCM), and it addresses the issue of consistency clustering in PCM. However, the clustering performance has not significantly improved, and its convergence speed is still slow. Hence, a total-aware suppressed possibilistic c-means (TSPCM) clustering is proposed in this paper. Firstly, from a global perspective, the typicality values of all the non-winners of samples inside the cluster core are decreased by the suppressed factor while increasing the typicality values of all the winners of samples in the same core. Meanwhile, the typicality values of samples outside the cluster core remain unchanged. The corrected typicality values are used to update the clustering centers, and the modified clustering centers are again used to renew the typicality values alternately until the algorithm converges. Secondly, the objective function of the proposed TSPCM is constructed, and Zangwill's theorem firmly establishes its convergence. Finally, two robust total-aware suppressed algorithms are presented when TSPCM is used for the clustering of numerical data with noise and the segmentation of noisy images. Experimental results show that the proposed TSPCM and its robust algorithms are obviously superior to existing state-of-the-art PCM-related algorithms. [ABSTRACT FROM AUTHOR]

Purpose: Multi-view fuzzy clustering algorithms are not widely used in image segmentation, and many of these algorithms are lacking in robustness. The purpose of this paper is to investigate a new algorithm that can segment the image better and retain as much detailed information about the image as possible when segmenting noisy images. Design/methodology/approach: The authors present a novel multi-view fuzzy c-means (FCM) clustering algorithm that includes an automatic view-weight learning mechanism. Firstly, this algorithm introduces a view-weight factor that can automatically adjust the weight of different views, thereby allowing each view to obtain the best possible weight. Secondly, the algorithm incorporates a weighted fuzzy factor, which serves to obtain local spatial information and local grayscale information to preserve image details as much as possible. Finally, in order to weaken the effects of noise and outliers in image segmentation, this algorithm employs the kernel distance measure instead of the Euclidean distance. Findings: The authors added different kinds of noise to images and conducted a large number of experimental tests. The results show that the proposed algorithm performs better and is more accurate than previous multi-view fuzzy clustering algorithms in solving the problem of noisy image segmentation. Originality/value: Most of the existing multi-view clustering algorithms are for multi-view datasets, and the multi-view fuzzy clustering algorithms are unable to eliminate noise points and outliers when dealing with noisy images. The algorithm proposed in this paper has stronger noise immunity and can better preserve the details of the original image. [ABSTRACT FROM AUTHOR]

Land cover classification of remote sensing image is faced with uncertainties such as "significant difference in category density", "the same object with different spectra", and "different objects with the same spectrum". Existing possibilistic fuzzy clustering-related method still cannot fully meet the interpretation requirements of remote sensing data. To accurately classify remote sensing image with complex geographical distribution, this paper proposes a robust interval type-2 dual-distance driven possibilistic fuzzy clustering motivated by interval-valued number for land cover classification. Firstly, interval-valued data model is established by using the local mean and variance of single-valued data. Secondly, Hausdorff distance and MW distance for interval-valued numbers are introduced to realize the maximum separability of overlapping categories in interval-valued data and generate interval uncertainty sets. To further enhance its robustness, weighted local information factors are constructed by making full use of the generated fuzzy membership and possibilistic typicality, and a novel robust interval type-2 possibilistic fuzzy C-means clustering is proposed. Finally, the adaptive type reduction method is introduced, meanwhile the adaptive expansion of interval-valued data model is realized by using the adaptive contraction expansion control factor. After the iterative algorithm convergences, the accurate classification of covered objects in remote sensing images is realized. Experimental results indicate that the classification performance of the proposed algorithm is better than that of existing interval type-2 fuzzy clustering algorithm and its variants, and it is more suitable for the interpretation of remote sensing images in the actual environments. [ABSTRACT FROM AUTHOR]

• An interval type-2 possibilistic fuzzy clustering with local information is proposed. • Deep neighborhood window is used to enhance the adaptive ability of algorithm. • Local and non-local information is used to improve the robustness of algorithm. • Using structural similarity to enhance the influence of local spatial information. • Testing results show that the proposed algorithm has extremely good performance. Type-2 fuzzy set theory has certain potential advantage in processing high-order uncertainty, and has been more and more widely applied in pattern recognition, image processing, and system modeling. Although it has been introduced into robust fuzzy clustering modeling, most clustering methods based on type-2 fuzzy set theory have many obvious shortcomings, such as weak robustness against noise and high sensitivity to initial value. Therefore, this paper presents a novel single fuzzifier interval type-2 kernel-based possibilistic fuzzy local and non-local information c-means clustering, and it is driven by deep neighborhood information for image segmentation in the presence of high noise. Firstly, we construct the novel deep neighborhood window structure, which consists of local neighborhood window around current pixel and deep neighborhood window around the pixel in local neighborhood window around current pixel. Secondly, using the structural similarity between local neighborhood window and deep neighborhood window around current pixel, we construct a novel fuzzy local information factor to tune the impact of local neighborhood information on current pixel clustering. Thirdly, possibility theory and fuzzy local information factor are introduced into interval type-2 kernel-based fuzzy c-means clustering, and a novel single fuzzifier type-2 possibilistic fuzzy clustering with richer local and non-local information and kernel metric is proposed. Experimental results indicate that the proposed algorithm outperforms existing state-of-the-art robust fuzzy clustering-related algorithms, and significantly improve the robustness of robust fuzzy clustering algorithms against noise. [ABSTRACT FROM AUTHOR]

Considering that weighted possibilistic fuzzy clustering does not obtain significant performance compared with possibilistic fuzzy clustering, so this paper proposes an enhanced self-adaptive weighted possibilistic fuzzy clustering algorithm. Firstly, the principle of maximum entropy is introduced to weighted possibilistic fuzzy clustering, and the weighted coefficients of fuzzy clustering and possibilistic clustering are subject to regularization entropy constraint and a novel self-learning iterative weighted possibilistic fuzzy clustering is obtained, and its convergence is strictly proved by Zangwill theorem and bordered Hessian matrix. Secondly, a series of clustering validity functions for the proposed algorithm are constructed to determine the optimal number of clusters in the data set. In the end, to enhance the anti-noise robustness of the proposed algorithm, a robust loss function is applied in the adaptive weighted possibilistic fuzzy clustering, and a robust algorithm is obtained for noisy data clustering. Experimental results show that the proposed algorithm outperforms existing possibilistic fuzzy clustering-related algorithms, and the validity functions for the proposed algorithm can accurately determine the optimal number of clusters in the data set, meanwhile, the corresponding robust algorithm effectively enhances the performance of the algorithm in the presence of noise. [ABSTRACT FROM AUTHOR]

• Fuzzy local information clustering with quadratic surface prototype is proposed. • Further fused with local membership to form dual local information constraints. • The convergence is analyzed by Zangwill theorem and bordered Hessian matrxi. • Experimental results show that the proposed algorithm has better robustness. Single-valued point can not effectively describe the gray characteristic of the image region, which makes existing fuzzy clustering algorithms difficult to segment image with uneven illumination, weak edges and strong noise. Hence, fuzzy c-means clustering with quadratic polynomial surface as clustering center has become a new method to solve complex image segmentation. Aiming at the shortcoming of current latest spatial fuzzy c-means clustering algorithm with quadratic surface as clustering center, quadratic polynomial surface is firstly introduced into existing widely used fuzzy local information c-means clustering algorithm, and further modify the membership degree of fuzzy local information c-means clustering by means of local membership of neighborhood pixels, a novel fuzzy dual-local information c-means clustering with quadratic polynomial-center clusters is proposed to solve the segmentation problem of complex image in the presence of high noise. Then the convergence of the proposed algorithm is strictly proved by Zangwill theorem and bordered Hessian matrix. Experimental results show that the proposed algorithm has better segmentation performance and anti-noise robustness than existing quadratic polynomial-related QPFCM, widely used FLICM, and multiple state-of-the-art algorithms, meanwhile, it also promotes the development of robust fuzzy clustering-related image segmentation theory. [ABSTRACT FROM AUTHOR]

Kernel possibilistic fuzzy C-means with local information (KWPFLICM) has important research significance of image segmentation, but it is very sensitive to high noise or outliers. To enhance the segmentation performance of the algorithm, this paper proposes a kernelized total Bregman divergence-driven possibilistic fuzzy clustering with local information (TKWPFLICM). Firstly, a polynomial kernel function is introduced to kernelize total Bregman divergence (TBD), and local neighborhood information of the pixel is used to modify it, which overcomes the shortcomings of Bregman divergence (BD) with rotation variability; Secondly, the modified kernelized TBD and possibilistic typicality are combined to further enhance the anti-noise ability of the algorithm; Finally, the modified kernelized TBD is introduced into the objective function of KWPFLICM algorithm, then a novel robust fuzzy clustering algorithm is derived by optimization theory. Experimental results show that compared with existing fuzzy clustering-related algorithms, the average SA improvement on TKWPFLICM algorithm is in the range of 0.791% to 33.237%. Therefore, TKWPFLICM algorithm has better anti-noise robustness and segmentation accuracy. [ABSTRACT FROM AUTHOR]

Many researchers have devoted themselves to studying image encryption based on chaotic system and have made significant strides in research in recent decades. This paper first combs and summarizes the development of the current image encryption algorithm. Then, the classification of chaotic system is described, and representative work and the latest achievements regarding image encryption algorithms based on chaotic system are analysed, expounding their advantages and disadvantages. Finally, we discuss future research directions and development trends in image encryption algorithms based on chaotic system. [ABSTRACT FROM AUTHOR]

In order to study a higher security encryption method, combined with thoughts of modern biological gene technology based on the DNA encoding, ZUC (ZU Chongzhi) algorithm and block encryption structure, this paper proposed a block encryption method on ZUC algorithm and DNA sequence. This algorithm firstly used the ZUC algorithm to generate the key stream of encrypt structure. Secondly, it encoded the plain-image and key stream by DNA encoding. At last, it controlled by the odevity of iteration time, divided the encoded plain-image into two parts, then encrypted it by the improved Feistel block structure which was based on the DNA operation, and obtained the cipher-image by DNA decoding. The theoretical analysis and experimental results show that the algorithm has high security and better statistical properties to resist selective attack and differential attack, and it is suitable for widely applying in the field of image information security protection. [ABSTRACT FROM AUTHOR]

Gaussian kernel is defined by Euclidean distance and has been widely used in many fields. In the view of Euclidean distance is sensitive to outliers or noise and it is difficult to obtain satisfactory results for complex non-convex data. Entropy-like divergence is firstly induced by combining Jenson-Shannon/Bregman divergence with convex function, and its mercer kernel function called entropy-like divergence-based kernel is also constructed in this paper. Secondly, a new fuzzy weighted factor based on entropy-like divergence-based kernel is proposed by improving the existing trade-off weighting factor of kernel-based fuzzy local information C-means clustering (KWFLICM). In the end, a weighted fuzzy local information clustering based on entropy-like divergence-based kernel (EKWFLICM) is presented, which combines a new weighted fuzzy factor and entropy-like divergence-based kernel. Experiment results show that the proposed algorithm outperforms the segmentation performance of existing state-of-the-art fuzzy clustering-related algorithms for the image in presence of high noise. • An entropy-like divergence and its mercer kernel function are presented. • A novel divergence-based kernel fuzzy clustering is proved to be convergent. • A kernel weighted fuzzy clustering based on entropy-like divergence is constructed. • Proposed algorithm outperforms existing KWFLICM and ILKFCM for image segmentation. [ABSTRACT FROM AUTHOR]

This paper proposes a block encryption algorithm based on a new chaotic system that combines generative adversarial networks (GANs) and DNA sequence coding. First, the new one-dimensional chaotic system that combines GANs with DNA sequence coding generates two more complex key stream sequences. Then, the two different random sequences are combined with an improved Feistel network by utilizing the product of the block matrix to encrypt the image to scramble and diffuse the image. Finally, the security performance of this algorithm is quantitatively analysed. The simulation results show that the proposed chaotic system has a large key space, and the new algorithm yields adequate security and can resist exhaustive attacks and chosen-plaintext attacks. Therefore, this approach provides a new algorithm for secure transmission and protection of image information. [ABSTRACT FROM AUTHOR]