Deep Learning for Screen-Shot Image Demoiréing: A Survey

Abstract

Image demoiréing is an important image processing technology in computer vision, used to remove the moiré from images and improve the image quality. In recent years, the image demoiréing technique based on the deep learning method has gained more attention and achieved good results, but it still has some limitations. This paper aims to provide a review and perspective on the recent advances in deep learning-based image demoiréing techniques. First, the definition and production principle of the image moiré pattern are given. Common datasets and image quality evalution methods in demoiréing studies are analyzed. Then two internationally famous competitions in image demoiréing are introduced. Second, the research status of the supervised demoiréing technique is summarized from four dimensions: sampling method, model network design, baseline model, and training learning strategy. Recent progress made by the mainstream model of unsupervised deep learning in the field of image demoiréing is summarized. The typical application of the image demoiréing technique in panel defect detection and digital radiography is analyzed. The performance and the image quality of the above mentioned models based on different data sets are evaluated in detail. Finally, this paper analyzes and forelocks the problems to be solved in the coming years.