Image Quality Enhancement using Deep learning-based Convolution Residual Networks Technique

A new approach for cleaning encrypted images is developed using Deep Convolutional Residual Network (Deep ConvResNet) as the proposed method. The aim of this research is to protect encrypted images from noise attacks by utilizing ResNet denoising capabilities. It has been proven that ResNets are successful at cleaning up noise while maintaining the important picture characteristics. This research employs multiple datasets for training and performs a detailed comparative study using peak signal to noise ratio and structure similarity index as well as noise and occlusion and blur attack metrics. Results of the simulation show that the suggested cryptosystem is resistant to the familiar attacks. Filtering based denoising techniques and CNNs are worse than ResNets because ResNets have better efficiency and resilience to occlusion and noise and blur attacks. The graphs of training loss vs. epoch show the convergence pattern of the model during training. Considering its potential use, this methodology is applicable in secure image transmission in different domains such as healthcare and multimedia transmission.