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Degradation regression with uncertainty for blind super-resolution

文献类型：期刊论文

作者Li, Shang1,3; Zhang, Guixuan2,3; Luo, Zhengxiong1,3; Liu, Jie2,3; Zeng, Zhi2; Zhang, Shuwu2,3
刊名NEUROCOMPUTING
出版日期2024-05-14
卷号582页码:14
关键词Blind super-resolution Degradation estimation Uncertainty learning
ISSN号0925-2312
DOI10.1016/j.neucom.2024.127486
通讯作者Zeng, Zhi(zhi.zeng@bupt.edu.cn)
英文摘要Some recent blind super -resolution (SR) efforts focus on designing complex degradation models to better simulate real -world degradations. The paired high -resolution (HR) & low -resolution (LR) samples synthesized by these models can cover a large degradation space, which helps train a robust SR model in real scenarios. However, these diverse synthetic samples may render the SR model degradation -unaware and prevent it from achieving optimal results on LR images with specific degradations. Alternatively, another category of methods is proposed to estimate specific degradations in the given application and then tailor a degradation -aware SR model accordingly. Nonetheless, degradation estimation is an ill -posed problem and accurate estimation is quite challenging. Towards these issues, we propose a probabilistic degradation estimator (PDE) which can predict the degradation as a certain distribution rather than a single point. Specifically, we develop an intersection over union (IoU) based degradation regression loss with uncertainty, which could lead PDE to shrink the possible degradation space of the test LR image. This enables the degradation model to synthesize more degradationspecific training samples and further improve SR performance. In this way, our PDE can alleviate degradation redundancy compared with degradation -unaware methods and is more robust to the degradation estimation error than previous degradation -aware methods. Extensive experiments show that the proposed PDE can help the SR model produce better results on both synthetic and real -world images.
WOS关键词IMAGE QUALITY ASSESSMENT ; NETWORK
资助项目National Key R&D Program of China[2022YFF0902202]
WOS研究方向Computer Science
语种英语
WOS记录号WOS:001221617100001
出版者ELSEVIER
资助机构National Key R&D Program of China
源URL[http://ir.ia.ac.cn/handle/173211/58397]  
专题数字内容技术与服务研究中心_新媒体服务与管理技术
通讯作者Zeng, Zhi
作者单位1.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100190, Peoples R China
2.Beijing Univ Posts & Telecommun, Sch Artificial Intelligence, Beijing 100876, Peoples R China
3.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China
推荐引用方式
GB/T 7714		 Li, Shang,Zhang, Guixuan,Luo, Zhengxiong,et al. Degradation regression with uncertainty for blind super-resolution[J]. NEUROCOMPUTING,2024,582:14.
APA Li, Shang,Zhang, Guixuan,Luo, Zhengxiong,Liu, Jie,Zeng, Zhi,&Zhang, Shuwu.(2024).Degradation regression with uncertainty for blind super-resolution.NEUROCOMPUTING,582,14.
MLA Li, Shang,et al."Degradation regression with uncertainty for blind super-resolution".NEUROCOMPUTING 582(2024):14.

入库方式： OAI收割

来源：自动化研究所

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