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Visually meaningful image encryption may keep the data security and appearance security of the digital images. However, there are still security and efficiency shortcomings existing in the current algorithms. To solve these problems, we propose an effective visually meaningful color image encryption scheme by combining hybrid multi-objective particle swarm optimization (HMPSO), block compressed sensing (BCS) and Hessenberg decomposition (HD). Firstly, the R, G, B components of color image are segmented averagely and represented sparsely by discrete cosine transform (DCT), respectively. Next, the obtained sparse images are scrambled by the use of zigzag path and measured by BCS to obtain the measurement value matrices. To improve its security, the key associated with the plain image is used as the initial value of the nonlinear chaotic system Henon, and a cross-component dislocation and diffusion strategy are applied to the measurements using the chaotic sequences generated by Henon to obtain the secret image, which enhances the ability of the algorithm to resist chosen-plaintext attack. Subsequently, the secret images are fused into the carrier image by the HD embedding algorithm to generate the final visually meaningful cipher image. In addition, in order to enhance the quality of the reconstructed image and cipher image, HMPSO is implemented to optimize the threshold value of sparse coefficient modification and the embedding rate simultaneously. Simulation results and performance analysis demonstrate the effectiveness, confidentiality and robustness of the proposed scheme.

In recent years, more and more deep learning frameworks are being applied to hyperspectral image classification tasks and have achieved great results. However, the existing network models have higher model complexity and require more time consumption. Traditional hyperspectral image classification methods tend to ignore the correlation between local spatial features. In this paper, a new hyperspectral image classification method is proposed, which combines two-dimensional Gabor filter with random patch convolution (GRPC) feature extraction to obtain spatial-spectral feature information. The method firstly performs dimensionality reduction through principal component analysis and linear discriminant analysis and extracts the edge texture and spatial information of the image using a Gabor filter for the reduced-dimensional image. Next, the extracted information is convolved with random patches to extract spectral features. Finally, the spatial features and multi-level spectral features are fused to classify the images using the Support Vector Machine classifier. In order to verify the performance of this method, experiments were conducted on three widely used datasets of Indian Pines, Pavia University and Kennedy Space Center. The overall classification accuracy reached 98.09%, 99.64% and 96.53%, which are all higher than other comparison methods. The experimental results reveal the superiority of the proposed method in classification accuracy.

Medical image encryption may help protect medical privacy. In this paper, we propose a new medical image encryption scheme combined Latin square and chaotic system. The architecture of permutation and diffusion is adopted. Using Latin square and the plain image information, permutation based on plain image and Latin square (PPILS) is presented to shuffle the pixels of the plain image to different rows and columns, effectively weaken the strong correlations between adjacent pixels, and different images have different permutation effect. To improve the encryption effect, bi-directional adaptive diffusion is proposed to spread little change of plain images to the entire pixels of cipher images. Chaotic sequences employed in permutation and diffusion are generated from the four-dimensional memristive chaotic system, its initial values are computed by SHA 256 hash value of the plain image, and thus the proposed algorithm may withstand known-plaintext and chosen-plaintext attacks. Simulation results and performance analyses show that our image encryption scheme has good security and robustness, and it may be applied for medical image encryption applications.

In this paper, an efficient image compression and encryption scheme combining the parameter-varying chaotic system, elementary cellular automata (ECA) and block compressive sensing (BCS) is presented. The architecture of permutation, compression and re-permutation is adopted. Firstly, the plain image is transformed by DWT, and four block matrices are gotten, and they are a low-frequency block with important information and three high-frequency blocks with less important information. Secondly, ECA is used to scramble the four sparse block matrices, which can effectively change the position of the elements in the matrices and upgrade the confusion effect of the algorithm. Thirdly, according to the importance of each block, BCS is adopted to compress and encrypt four scrambled matrices with different compression ratios. In the BCS, the measurement matrices are constructed by a parameter-varying chaotic system, and thus few parameters may produce the large measurement matrices, which may effectively reduce memory space and transmission bandwidth. Finally, the four compressed matrices are recombined into a large matrix, and the cipher image is obtained by re-scrambling it. Moreover, the initial values of the chaotic system are produced by the SHA 256 hash value of the plain image, which makes the proposed encryption algorithm highly sensitive to the original image. Experimental results and performance analyses demonstrate its good security and robustness.

At present, information entropies of cipher images gotten by some CS-based image cryptosystems are lower than 7, which make them vulnerable to entropy attack. To cope with this problem, we propose a novel image compression–encryption method based on compressive sensing (CS) and game of life (GOL). Encryption architecture of permutation, compression and diffusion is utilized. Firstly, a plaintext-dependent game-of-life-based scrambling method is presented to shuffle the sparse coefficient matrix of plain image, and the permutation matrix is constructed by rules of GOL, which may effectively reduce the adjacent pixel correlation and enhance the scrambling effect. Secondly, the confused matrix is compressed by CS and diffused using a key matrix to get the cipher image. Additionally, a five-dimensional (5D) memristive hyperchaotic system is used to generate chaotic sequences. They are utilized to construct measurement matrix, to generate initial cell matrix of GOL and to produce key matrix. Information entropy of plain image and external key parameters are combined to compute initial values of the hyperchaotic system. Therefore, our algorithm has high sensitivity to original image and it may resist against known-plaintext attack and chosen-plaintext attack. Experimental results and performance analyses demonstrate that the proposed encryption algorithm is effective to withstand various typical attacks, and it may be applied for image secure communication.

Compared to 1D compressed sensing (CS), 2D CS is more efficient for compressing the plaintext image from two directions, but security level of current 2D CS-based ciphers is unsatisfactory. To solve this problem, this paper presents a novel color image compression and encryption algorithm by combining 2D CS, information entropy and chaos. Firstly, the color image is decomposed into red, green and blue components, then they are sparsely transformed by the discrete wavelet transform (DWT) to get three sparse matrices. Next, the obtained matrices are observed by two asymptotical deterministic random measurement matrices based on information entropy and counter (ADMMIC), which not only encrypts the plaintext image, but also compresses it in proportion to reduce the transmission bandwidth and storage space. Subsequently, the corresponding measurement value matrices are shuffled by a double random scrambling based on Arnold map and index vector (DRSAIV) to eliminate the correlation between adjacent pixels. Furthermore, the obtained permutated matrices are diffused by a simultaneous multiple random diffusion of inter–intra components (SMRDIC) to obtain the final cipher image, the plaintext pixel to be diffused, the key matrix involved in diffusion and the position of the obtained ciphertext pixel are all unpredictable, which makes statistical attack invalid. In addition, information entropy values of plaintext image are obtained to generate the initial values of the used chaotic systems, which greatly improve the ability to resist the known-plaintext and chosen-plaintext attacks. Simulation results and security analyses verify that this algorithm has good compression and high security.

Nowadays, many image cryptosystems have been cracked by chosen-plaintext attacks, for they are not highly sensitive to plain image. To solve this problem, we introduce a plaintext-related mechanism for secure color image encryption, and it is established in the generation and selection of chaotic sequences, permutation and diffusion. In the proposed image cryptosystem, the architecture of permutation and diffusion is adopted. Firstly, plaintext-related Latin-square-based block permutation is proposed to randomly shuffle pixels of the color plain image, diffusion method dependent on the plaintext and scrambled image is further given to modify pixels of permutated image, and finally cipher image is gotten. The chaotic sequence for diffusing the current pixel is dynamically generated according to the plain image and scrambled image, and diffusion operations of red, green and blue components of color plain image affect each other. Besides, chaotic sequences used in encryption are produced by new one-dimensional chaotic systems and dynamically selected, and initial values of chaotic systems are computed by plain image and external keys. Experimental results and security analyses demonstrate that our image encryption has large key space and high security level, and it can be applied for the secure communication of image information.

In this paper, an image encryption algorithm based on 3-D DNA level permutation and substitution scheme is proposed. In order to improve the security level of the image encryption, a 3-D DNA level hybrid permutation scheme using chaotic sequence sorting and 3-D cat map is presented to effectively shuffle the element positions, a novel 3-D DNA level substitution method based on random number is given to directly change the elements of the 3-D DNA matrix, and dynamic and random DNA encoding rules and DNA decoding rules are designed to upgrade the randomness of the encryption results. Moreover, a 6-D hyperchaotic system is used to generate the chaotic sequence for all the encryption processes, and SHA 256 hash value of the plain image is utilized to compute the initial values of chaotic system and system parameters of the 3-D cat map. Simulation results and performance analyses demonstrate that the proposed encryption scheme has large key space, high key sensitivity and may resist against some typical attacks, and it may effectively secure the secret image information.

Magnetic resonance (MR) angiography is one of the main diagnostic approaches for cardiac-cerebral vascular diseases. Nevertheless, the non-contrast-enhanced MR angiography suffers from its intrinsic problems derived from the blood flow-dependency, while the clinical Gd-chelating contrast agents are limited by their rapid vascular extravasation. Herein, we report a hypersensitive MR angiography strategy based on interlocking stratagem of zwitterionic Gd-chelate contrast agents (PAA-Gd). The longitudinal molar relaxivity of PAA-Gd was 4.6-times higher than that of individual Gd-chelates as well as appropriate blood half-life (73.8 min) and low immunogenicity, enabling sophisticated micro-vessels angiography with a resolution at the order of hundred micrometers. A series of animal models of cardiac-cerebrovascular diseases have been built for imaging studies on a 7.0 T MRI scanner, while the clinical translation potential of PAA-Gd has been evaluated on swine on a 3.0 T clinical MRI scanner. The current studies offer a promising strategy for precise diagnosis of vascular diseases. Current contrast-enhanced magnetic resonance angiography approaches are sub-optimal. Here the authors present a hypersensitive MR angiography strategy based on interlocking stratagem of zwitterionic Gd-chelate contrast agents (PAA-Gd), enabling sophisticated micro-vessel angiography of cardiac-cerebrovascular diseases with ultrahigh resolution.

Benefiting from its high thermal conductivity, simple structure, and light weight, the pulsating heat pipe (PHP) can meet the requirements for high efficiency, flexibility, and low cost in industrial heat transfer applications such as aerospace detector cooling and vehicle thermal management. Compared to a PHP working at room temperature, the mechanism of a PHP with hydrogen as the working fluid differs significantly due to the unique thermal properties of hydrogen. In this paper, a two-dimensional model of a hydrogen PHP with a filling ratio of 51% was established to study the flow characteristics and thermal performance. The volume of fluid (VOF) method was used to capture the phase distribution and interface dynamics, and the Lee model was employed to account for phase change. To validate the model, a comparison was conducted between the simulation results and experimental data obtained in our laboratory. The simulation results show that the pressure and temperature errors were within 25% and 5%, respectively. Throughout a pressure oscillation cycle, the occurrence of uniform flow velocity, acceleration, and flow reversal can be attributed to the changes in the vapor–liquid phase distribution resulting from the effect of condensation and evaporation. In addition, when the fluid velocity was greater than 0.6 m/s, dynamic contact angle hysteresis was observed in the condenser. The results contribute to a deeper understanding of the flow and heat transfer mechanism of the hydrogen PHPs, which have not been yet achieved through visualization experiments.