Research advanced in deep learning-based image restoration

Abstract

In the computer vision community, which tries to address issues with image data change, loss, and missing that may arise in the process of image collecting, transmission, and preprocessing, image restoration has always been a hot study topic.Learning the contextual information of the visible regions in an image is the basic idea of image restoration algorithms, which perform noise removal, missing region repair and transformation on the regions to be restored to reproduce the original image content. Most of the early image restoration algorithms rely on the design of manual features, whose accuracy and generalization ability can not meet the practical application requirements. Due to the rapid advancement of CNN, in recent years, image repair accuracy and effect have made breakthroughs, and have been widely used in old photos, movies, videos and other repair scenes. In this paper, three restoration algorithms (sequence-based restoration, CNN-based restoration and GAN-based restoration) are comprehensively introduced while the research progress of image restoration based on depth learning is reviewed. Secondly, we quantitatively compare the performance of different repair algorithms and analyze their advantages and disadvantages. Finally, we summarize the challenges facing the area of image restoration research and evaluate its potential future directions.