End-to-End Alternating Optimization for Real-World Blind Super Resolution

doi:10.1007/s11263-023-01833-7

	End-to-End Alternating Optimization for Real-World Blind Super Resolution
	Luo, Zhengxiong1,2,3 ; Huang, Yan2,3 ; Li, Shang1,2 ; Wang, Liang2,3,4 ; Tan, Tieniu2,3,5
发表期刊	INTERNATIONAL JOURNAL OF COMPUTER VISION
ISSN	0920-5691
	2023-08-03
页码	18
通讯作者	Huang, Yan(yhuang@nlpr.ia.ac.cn)
摘要	Blind super-resolution (SR) usually involves two sub-problems: (1) estimating the degradation of the given low-resolution (LR) image; (2) super-resolving the LR image to its high-resolution (HR) counterpart. Both problems are ill-posed due to the information loss in the degrading process. Most previous methods try to solve the two problems independently, but often fall into a dilemma: a good super-resolved HR result requires an accurate degradation estimation, which however, is difficult to be obtained without the help of original HR information. To address this issue, instead of considering these two problems independently, we adopt an alternating optimization algorithm, which can estimate the degradation and restore the SR image in a single model. Specifically, we design two convolutional neural modules, namely Restorer and Estimator. Restorer restores the SR image based on the estimated degradation, and Estimator estimates the degradation with the help of the restored SR image. We alternate these two modules repeatedly and unfold this process to form an end-to-end trainable network. In this way, both Restorer and Estimator could get benefited from the intermediate results of each other, and make each sub-problem easier. Moreover, Restorer and Estimator are optimized in an end-to-end manner, thus they could get more tolerant of the estimation deviations of each other and cooperate better to achieve more robust and accurate final results. Extensive experiments on both synthetic datasets and real-world images show that the proposed method can largely outperform state-of-the-art methods and produce more visually favorable results.
关键词	Blind super resolution Degradation estimation Alternating optimization Restorer Estimator
DOI	10.1007/s11263-023-01833-7
关键词[WOS]	IMAGE SUPERRESOLUTION ; NETWORK ; DARK
收录类别	SCI
语种	英语
WOS研究方向	Computer Science
WOS类目	Computer Science, Artificial Intelligence
WOS记录号	WOS:001042003400001
出版者	SPRINGER
引用统计
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/53928
专题	多模态人工智能系统全国重点实验室
通讯作者	Huang, Yan
作者单位	1.Univ Chinese Acad Sci, Artificial Intelligence Sch, Beijing 100049, Peoples R China 2.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China 3.Ctr Res Intelligent Percept & Comp, Beijing, Peoples R China 4.Ctr Excellence Brain Sci & Intelligence Technol, Beijing, Peoples R China 5.Nanjing Univ, Nanjing, Peoples R China
第一作者单位	中国科学院自动化研究所
通讯作者单位	中国科学院自动化研究所
推荐引用方式 GB/T 7714	Luo, Zhengxiong,Huang, Yan,Li, Shang,et al. End-to-End Alternating Optimization for Real-World Blind Super Resolution[J]. INTERNATIONAL JOURNAL OF COMPUTER VISION,2023:18.
APA	Luo, Zhengxiong,Huang, Yan,Li, Shang,Wang, Liang,&Tan, Tieniu.(2023).End-to-End Alternating Optimization for Real-World Blind Super Resolution.INTERNATIONAL JOURNAL OF COMPUTER VISION,18.
MLA	Luo, Zhengxiong,et al."End-to-End Alternating Optimization for Real-World Blind Super Resolution".INTERNATIONAL JOURNAL OF COMPUTER VISION (2023):18.