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Medical images contain sensitive information and are prone to various attacks during network transmission. Therefore, it is essential to develop a robust algorithm to ensure the safety of transmitting medical images. In this paper, we propose an efficient and secure color medical image encryption system utilizing the FRFT-DRPE optical transformation and 2D-SICM hyperchaos system. The encryption process in our system considers the specific characteristics of each channel in the HSI color model. First, the image is converted from RGB space to HSI space. Subsequently, different encryption methods are applied to the hue and saturation components, including S-box replacement, scrambling, and diffusion operations. For the intensity component, scrambling and FRFT-DRPE optical encryption operations are performed. Additionally, the order of 2D-FRFT serves as an additional key to enhance the security of the encryption system. Finally, the three channels are merged to complete the overall encryption process. Simulations and performance analysis have confirmed the high-security level of our algorithm. It effectively protects the privacy of medical images during transmission. [ABSTRACT FROM AUTHOR]

Medical images contain sensitive information that will be shared with healthcare workers, patients, and researchers for diagnostic and research purposes. However, the security of these images during transmission is extremely important, especially after the COVID-19 pandemic. In diagnosis, MRI and CT images are two common medical images, and the two medical images complement each other to a certain extent. The fusion of these two images can help doctors diagnose more intuitively. This paper proposes a zero-watermarking encryption scheme with an authentication function based on multimodal medical image fusion. In addition, an improved three-dimensional Chebyshev and ICMIC closed-loop coupling mapping (3D-CICCM) is proposed and applied to the design of medical image encryption. This scheme mainly includes: medical image fusion based on bilateral filtering and local gradient energy, authentication based on the Fresnel transform, double random phase encryption based on the Fresnel transform and shuffle algorithm, and zero-watermark generation algorithm based on stationary wavelet transform. The experimental results prove the effectiveness of this scheme. [ABSTRACT FROM AUTHOR]

This paper proposes a lossless encryption–compression algorithm for large-scale remote sensing images. Firstly, the red, green and blue components of color image are compressed by a lossless predictive encoding. Then, the lifting wavelet transform (LWT) is used to decompose the encoding results. And a new Rubik's cube transformation is introduced to scramble the decomposed coefficients, which uses the chaotic sequence generated by 2D Cubic–Chebyshev map (2D-CCM). The initial values of 2D-CCM are obtained from the chi-square test values of the three components, which leads to the algorithm related to the plaintext image. After that, the scrambling coefficients are thresholding, and the position sequences generated in the process are encrypted and compressed by the proposed encrypted run-length encoding (E-RLE). The processed coefficients are further compressed by Huffman encoding. At the end, the final results are obtained by a novel helix diffusion which is related to the chaotic sequence. Experimental results show that, this algorithm achieves higher lossless compression ratio with lower time complexity, and the encryption scheme has higher security. [ABSTRACT FROM AUTHOR]

With the advancement of remote sensing technology, the amount of remote sensing image (RSI) has increased sharply. The explosion in data volume requires higher standards of image compression and encryption technology. This paper proposes a lossless and lossy encryption-compression method for RSI. Because RSIs contain important and unimportant information, using a normal algorithm could ignore their important information or reduce the computational efficiency, so we use the trained model by DeepLabv3+ to segment the region of interest (ROI) and the region of non-interest (RONI), and implement lossless and lossy algorithms respectively. The whole algorithm is based on the newly proposed hyperchaotic system 2D Tent coupled Infinite collapse map (2D TICM). According to the 2D TICM, the dynamic 3D Latin cube is generated. This cube is then used for dynamic scrambling algorithms between multiple planes (3D LMBS), within planes (3D LMIS), and for the multiple dynamic S boxes substitution algorithm. Due to the large size of RSI, the image is divided into blocks, and MATLAB parallel mechanism is used to encrypt each block at the same time. First, the lossy part is scrambled by 3D LMIS, then compressed with 2D compressive sensing (2D CS). Finally, each block is substituted with a different S box. A CS reconstruction algorithm combining decryption and reconstruction, 2D projection gradient chaotic decryption algorithm (2D PG-CD), is proposed. The lossless part embeds the encryption into JPEG-LS, encrypts while compressing, and then encrypts according to 3D LMBS and S boxes. The algorithm in this paper considers the characteristics of RSI, experiments show that it has good security and compression performance. • A hyperchaotic system 2D Tent coupled infinite collapse map (2D-TICM) is proposed. • Lossy compression 2D CS and 2D PG-CD improves the computational efficiency. • Encryption step is integrated into JPEG-LS and compression to retain important information. • Dynamic 3D Latin cube is designed with chaos and multiple S boxes substitution are proposed. [ABSTRACT FROM AUTHOR]