Blind Image Decomposition
Junlin Han
Weihao Li
Pengfei Fang
Chunyi Sun
Jie Hong
Ali Armin
Lars Petersson
Hongdong Li


We propose and study a novel task named Blind Image Decomposition (BID), which requires separating a superimposed image into constituent underlying images in a blind setting, that is, both the source components involved in mixing as well as the mixing mechanism are unknown. For example, rain may consist of multiple components, such as rain streaks, raindrops, snow, and haze. Rainy images can be treated as an arbitrary combination of these components, some of them or all of them. How to decompose superimposed images, like rainy images, into distinct source components is a crucial step towards real-world vision systems. To facilitate research on this new task, we construct multiple benchmark datasets, including mixed image decomposition across multiple domains, real-scenario deraining, and joint shadow/reflection/watermark removal. Moreover, we propose a simple yet general Blind Image Decomposition Network (BIDeN) to serve as a strong baseline for future work. Experimental results demonstrate the tenability of our benchmarks and the effectiveness of BIDeN.



The architecture of Blind Image Decomposition Network (BIDeN).



Han et al.
Blind Image Decomposition
ECCV, 2022
(hosted on ArXiv)



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