GAN and MIRNet-Assisted Image Enhancement: A Promising Future in Visual Computing

Abstract

Abstract: We present an extensive strategy for improving images using deep learning-based techniques, concentrating onsuperresolution (SR) and low-light image improvement utilizing generative adversarial networks (GANs) and misalignment-robust networks (MIRNet), respectively. During the SGAN training phase, a deep convolution neural network learns a complete link between low- and high-resolution pictures. Our approach simultaneously optimises all layers to provide cutting-edge restoration quality and quick speed for practical application. To achieve performance and speed trade-offs, we also investigate alternative network setups andparameter settings.Additionally, we upgrade the overall reconstruction quality by expanding our network to handlethree color channels concurrently. In contrast, MIRNet is utilized for low light image improvement, which has grown in significance as a result of the increased need for trustworthy picture enhancement systems in real-time applicationsincluding autonomous driving, surveillance footage, and crime scene investigations. MIRNet, which may be used for a range of image improving applications.