An effective secret image sharing using quantum logic and GPGPU based EDNN super-resolution

Abstract

This paper presented an effective secret image sharing with super-resolution utilizing quantum logic and enthalpy based adaptive deep neural network. The proposed technique is processed as; at the sender side, initially secret input image is converted into a halftone image format by utilizing Error diffusion with varying thresholds (EDVT) method. Then in share generation phase, shares are produced with the basis matrix. Here, the basis matrix is created utilizing the quantum logic methodology. Then in embedding phase, discrete wavelet transform (DWT) is utilized for encoding shares. At the receiver side, encoded image is reconstructed using XOR operation and results the low-resolution image. Finally, enthalpy based adaptive deep neural network (EDNN) is designed with the General Purpose Graphic Processing Unit (GPGPU) to enhance the resolution of the reconstructed images and to lessen the time complexity of deep learning. Here, the EDNN is adapted with the enthalpy based normalization to mitigate the over fitting in layers of deep neural network. Furthermore, the proficiency of the proposed work improved in terms of normalized cross correlation, normalized absolute error, peak signal to noise ratio, mean square error and execution time by deploying images among CPU and GPGPU in an enhanced manner. © 2020, Springer Science+Business Media, LLC, part of Springer Nature.