Intensified Convolutional Neural Network for Enhancing Quality of Encrypted and Compressed Optical Images

Abstract

The compression and encryption of images is essential to securely transmit high quality images over the optical network. In earlier studies, an optical image encryption using Loxodromic cat map with improved double random phase encoding (LCMIDRPE) has been developed. Hilbert huang transform (HHT) used in this encoding model decrease deviations of encrypted images. However, efficient image compression technique is required for compression. An efficient optical image compression and encryption (OICE) technique is developed in this paper by Direction-adaptive discrete wavelet transform (DA-DWT) along with directional lifting and LCMIDRPE. The original image is compressed using the DA-DWT with directional lifting and then encrypted by LCMIDRPE. The inverse version of LCMIDRPE and DA-DWT regain the original image. The missed information’s due to lossy compression, decompression, encryption, decryption and transmission of images degrade the quality of regained images. The regained image is enhanced using intensified convolutional neural network (ICNNet). The whole process is termed to be LCMIDRPE-DADWT-ICNNet. Finally, the experimental results exhibits that the LCMIDRPE-DADWT-ICNNet model achieves correlation coefficient (CC) of 0.98, Peak signal-to-noise ratio (PSNR) of 97db, Mean absolute difference (MAD) of 20 and mean square error (MSE) of 0.45 which is higher than OICE methods like Compressive Sensing and Rivest-Shamir-Adleman method (CS-RSA), Fresnel Diffraction and Discrete Wavelet Transform (FDDWT), 2Dimentional Sparse Representation and Chaotic Map (2DSR-CM), 2Dimensional Compressive Sensing and Hyperchaotic System (2DCS-HS), Diffractive-Imaging-Based Encryption (DIBE) and Content-Adaptive Image Compression and Encryption using Optimized Compressive Sensing with Double Random Phase Encoding (CAIEOCS-DPRE) methods.