A multi-image optical encryption method based on wavelet compression and deep learning reconstruction was proposed to solve the problems such as large ciphertext,poor encryption effect and unsatisfactory reconstruction effect.The wavelet com-pression was used to extract the low-frequency parts of multiple images,and the new images were put into the improved FDT-DRPE optical encryption system to get the ciphertext.The insensitivity of FDT-DRPE was overcome by vector decomposition and helical phase transformation.The constructed L_S chaotic system improved the key sensitivity.A deep learning network model BHCN was proposed to solve the problem of low accuracy of traditional image reconstruction.Experimental results show that the volume of ciphertext can be compressed to 1/4 of the original image,the peak signal-to-noise ratio of reconstructed image is 34.57 dB,and the structural similarity is 0.9521.Compared with similar literatures,the reconstruction speed is higher,the reconstruction effect is better,and the security is higher.
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