Paper Info
Title
Deep Learning for Screen-Shot Image Demoireing: A Survey
Abstract
Image demoireing is an important image processing technology in computer vision, used to remove the moire from images and improve the image quality. In recent years, the image demoireing 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 demoireing techniques. First, the definition and production principle of the image moire pattern are given. Common datasets and image quality evalution methods in demoireing studies are analyzed. Then two internationally famous competitions in image demoireing are introduced. Second, the research status of the supervised demoireing 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 demoireing is summarized. The typical application of the image demoireing 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.
Year
DOI
Venue
2022
10.1109/ACCESS.2022.3213025
IEEE ACCESS
Keywords
DocType
Volume
Image resolution, Image quality, Deep learning, Convolutional neural networks, Visualization, Image restoration, Web pages, Image demoireing, screen-shot image, deep learning, convolutional neural networks (CNN)
Journal
10
ISSN
Citations 
PageRank 
2169-3536
0
0.34
References 
Authors
0
6
Name
Order
Citations
PageRank
Shouming Hou100.34
Yabing Wang200.34
Kai Li324.49
Yinggang Zhao400.34
Baoyun Lu500.34
Liya Fan600.34