Multi-frame GAN-based Machine Learning Image Restoration for Degraded Visual Environments

Although single-frame machine learning image restoration techniques have been shown to be effective, the proposed multi-frame approach takes advantage of both spatial and temporal information to resolve high-resolution and high-dynamic-range images. The proposed algorithm is an extension of the previously proposed algorithm DeblurGAN-C and aims to further improve the capabilities of image restoration in degraded visual environments. The main contributions of the proposed techniques include: 1) Development of an effective framework to generate a multi-frame training dataset typical of degraded visual environments; 2) Adopting a multi-frame image restoration framework that generates a single restored image as the output; 3) Conducting substantial experiments against the generated multi-frame training dataset and demonstrate the effectiveness of the proposed multi-frame image enhancement algorithm.