Adnan Malik¹, Muhammad Ali¹, Faisal S. Alsubaei², Nisar Ahmed^3,*, Harish Kumar⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.1, pp. 965-999, 2023, DOI:10.32604/cmes.2023.022493

Abstract The security of digital images transmitted via the Internet or other public media is of the utmost importance. Image encryption is a method of keeping an image secure while it travels across a non-secure communication medium where it could be intercepted by unauthorized entities. This study provides an approach to color image encryption that could find practical use in various contexts. The proposed method, which combines four chaotic systems, employs singular value decomposition and a chaotic sequence, making it both secure and compression-friendly. The unified average change intensity, the number of pixels’ change rate, information entropy analysis, correlation coefficient analysis,… More >

Gunnam Suryanarayana^1,*, D. Bhavana², P. E. S. N. Krishna Prasad³, M. M. K. Narasimha Reddy¹, Md Zia Ur Rahman²

CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 1123-1137, 2023, DOI:10.32604/cmc.2023.031227

Abstract Single image super resolution (SISR) techniques produce images of high resolution (HR) as output from input images of low resolution (LR). Motivated by the effectiveness of deep learning methods, we provide a framework based on deep learning to achieve super resolution (SR) by utilizing deep singular-residual neural network (DSRNN) in training phase. Residuals are obtained from the difference between HR and LR images to generate LR-residual example pairs. Singular value decomposition (SVD) is applied to each LR-residual image pair to decompose into subbands of low and high frequency components. Later, DSRNN is trained on these subbands through input and output… More >