A visually secure image encryption scheme based on adaptive block compressed sensing and non-negative matrix factorization

Shi, Yuandi; Chen, Rongrong; Liu, Donglin; Wang, Bin

doi:10.1016/j.optlastec.2023.109345

Cited by 16 publications

(2 citation statements)

References 38 publications

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“…Optical remote sensing imaging technology is an important branch of remote sensing technology, serving as the "eyes" for human exploration of the universe and the unknown world. Satellite remote sensing image data with "four highs" characteristics of high space, high time, high spectrum and high radiation resolution provides important information and services for human monitoring and research on the earth's environment and resources [1][2][3][4][5]. The traditional remote sensing satellite image signal acquisition process uses an optical remote sensing camera to perform Nyquist full sampling imaging on the target, then converts the collected analog signal to digital, and finally compresses and encodes the image information through digital processing [6,7].…”

Section: Introductionmentioning

confidence: 99%

Full-Process Adaptive Encoding and Decoding Framework for Remote Sensing Images Based on Compression Sensing

Hu,

Liu,

Liu

et al. 2024

Remote Sensing

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Faced with the problem of incompatibility between traditional information acquisition mode and spaceborne earth observation tasks, starting from the general mathematical model of compressed sensing, a theoretical model of block compressed sensing was established, and a full-process adaptive coding and decoding compressed sensing framework for remote sensing images was proposed, which includes five parts: mode selection, feature factor extraction, adaptive shape segmentation, adaptive sampling rate allocation and image reconstruction. Unlike previous semi-adaptive or local adaptive methods, the advantages of the adaptive encoding and decoding method proposed in this paper are mainly reflected in four aspects: (1) Ability to select encoding modes based on image content, and maximizing the use of the richness of the image to select appropriate sampling methods; (2) Capable of utilizing image texture details for adaptive segmentation, effectively separating complex and smooth regions; (3) Being able to detect the sparsity of encoding blocks and adaptively allocate sampling rates to fully explore the compressibility of images; (4) The reconstruction matrix can be adaptively selected based on the size of the encoding block to alleviate block artifacts caused by non-stationary characteristics of the image. Experimental results show that the method proposed in this article has good stability for remote sensing images with complex edge textures, with the peak signal-to-noise ratio and structural similarity remaining above 35 dB and 0.8. Moreover, especially for ocean images with relatively simple image content, when the sampling rate is 0.26, the peak signal-to-noise ratio reaches 50.8 dB, and the structural similarity is 0.99. In addition, the recovered images have the smallest BRISQUE value, with better clarity and less distortion. In the subjective aspect, the reconstructed image has clear edge details and good reconstruction effect, while the block effect is effectively suppressed. The framework designed in this paper is superior to similar algorithms in both subjective visual and objective evaluation indexes, which is of great significance for alleviating the incompatibility between traditional information acquisition methods and satellite-borne earth observation missions.

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Section: Introductionmentioning

confidence: 99%

Full-Process Adaptive Encoding and Decoding Framework for Remote Sensing Images Based on Compression Sensing

Hu,

Liu,

Liu

et al. 2024

Remote Sensing

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“…Key sensitivity: Chaos-based encryption algorithms exhibit key sensitivity, indicating that even a slight alteration to the encryption key results in a significantly altered encrypted picture. This characteristic improves the system's security and resilience to brute-force assaults [6].…”

Section: Introductionmentioning

confidence: 99%

A High-Security Image Utilizing Triple Generators for the Rabinovitch System

2023

IJIES

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Chaotic encryption offers a higher level of security than traditional ciphers. This research provides an encryption system using chaotic sequences to encrypt digital images using three hyperchaotic methods. The first system generates a random integer number sequence between (0 -255), while the second and third hyper-chaotic generators use a chaotic random selector from the random number generated from the first hyperchaotic generator and the image pixels. The third system is used to chaotic select the pixel from the image. Then the pixel value is changed using the XOR operation to encrypt the pixels. The number of keys depends on 33 dimensions (each hyper-chaos has 11 dimensions, four initial values, five parameters, and two big and mod numbers), and all available keys at less than 2 1644 . This number represents a massive key space.

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Deep learning and chaotic system based image encryption algorithm for secondary user system

Jin,

Lei,

Liu

2024

Nonlinear Dyn

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A visually secure image encryption scheme based on adaptive block compressed sensing and non-negative matrix factorization

Cited by 16 publications

References 38 publications

Full-Process Adaptive Encoding and Decoding Framework for Remote Sensing Images Based on Compression Sensing

Full-Process Adaptive Encoding and Decoding Framework for Remote Sensing Images Based on Compression Sensing

A High-Security Image Utilizing Triple Generators for the Rabinovitch System

Deep learning and chaotic system based image encryption algorithm for secondary user system

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