  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 toward
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.
