Image Encryption based on Compressed Sensing, Chaotic System and Slant Haar Type Orthogonal Transform

In the era of big data, how to encrypt image and ensure image security is an important research hot spot. In this paper, an efficient image encryption scheme is put forward based on Slant Haar Type Orthogonal transform (SHTOT) and compressed sensing (CS) combined with chaotic system. First, the original image is transformed by our proposed SHTOT, which contains specific parameters that may be regarded as encryption key. Then, the transformed coefficients are compressed and measured simultaneously by using CS, during which some pseudo random sequences produced by a chaotic system coupling sine mapping and logistic mapping are employed to generate the measurement matrix for CS. Next, Arnold transform is utilized to the scrambling of the CS measured values. Based on this, some other pseudo random sequences are used to the modification of the quantized CS measured values. Finally, the decryption operation is performed according to the reverse process described above and a blind Sparsity Adaptive Matching Pursuit algorithm in CS is applied to the image reconstruction. Simulation and experimental analysis demonstrate that the image encryption scheme provided in this paper has good performance in the image compression and encryption from the perspective of visual effect, information entropy, correlation coefficient, key sensitivity, key space and robustness.