{"title":"Enhanced local distribution learning for real image super-resolution","authors":"","doi":"10.1016/j.cviu.2024.104092","DOIUrl":null,"url":null,"abstract":"<div><p>Previous work has shown that CNN-based local distribution learning can efficiently reconstruct high-resolution images, but with limited performance improvement against complex degraded images. In this paper, we propose an enhanced local distribution learning framework, called ELDRN, which successfully generalizes local distribution learning to realistic images whose degradation process is complex and unknowable. The cores of our ELDRN are the parallel attention block and dilated neighborhood sampling. The former mines discriminative features at both spatial and channel levels, that is, parameters for constructing local distributions, thus improving the robustness of distributions to real degradation patterns. To deal with the fact that the reference range of the target sub-pixel is not exactly equal to its neighborhood, we explicitly increase the sampling density, <em>i.e.</em>, fusing more sampled pixels to produce the target sub-pixel. Experiments conducted on RealSR dataset illustrate that our ELDRN outperforms recent learning-based SISR methods and reconstructs visually-pleasant high-quality images.</p></div>","PeriodicalId":50633,"journal":{"name":"Computer Vision and Image Understanding","volume":null,"pages":null},"PeriodicalIF":4.3000,"publicationDate":"2024-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computer Vision and Image Understanding","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1077314224001735","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0
Abstract
Previous work has shown that CNN-based local distribution learning can efficiently reconstruct high-resolution images, but with limited performance improvement against complex degraded images. In this paper, we propose an enhanced local distribution learning framework, called ELDRN, which successfully generalizes local distribution learning to realistic images whose degradation process is complex and unknowable. The cores of our ELDRN are the parallel attention block and dilated neighborhood sampling. The former mines discriminative features at both spatial and channel levels, that is, parameters for constructing local distributions, thus improving the robustness of distributions to real degradation patterns. To deal with the fact that the reference range of the target sub-pixel is not exactly equal to its neighborhood, we explicitly increase the sampling density, i.e., fusing more sampled pixels to produce the target sub-pixel. Experiments conducted on RealSR dataset illustrate that our ELDRN outperforms recent learning-based SISR methods and reconstructs visually-pleasant high-quality images.
期刊介绍:
The central focus of this journal is the computer analysis of pictorial information. Computer Vision and Image Understanding publishes papers covering all aspects of image analysis from the low-level, iconic processes of early vision to the high-level, symbolic processes of recognition and interpretation. A wide range of topics in the image understanding area is covered, including papers offering insights that differ from predominant views.
Research Areas Include:
• Theory
• Early vision
• Data structures and representations
• Shape
• Range
• Motion
• Matching and recognition
• Architecture and languages
• Vision systems