Related papers: Blind Image Decomposition

Blind Image Decomposition

URL: http://arxiv.org/abs/2108.11364v1
Date: Wed, 25 Aug 2021 17:37:19 GMT
Title: Blind Image Decomposition
Authors: Junlin Han, Weihao Li, Pengfei Fang, Chunyi Sun, Jie Hong, Mohammad Ali Armin, Lars Petersson, Hongdong Li
Abstract summary: We present Blind Image Decomposition (BID), which requires separating a superimposed image into constituent underlying images in a blind setting. How to decompose superimposed images, like rainy images, into distinct source components is a crucial step towards real-world vision systems. We propose a simple yet general Blind Image Decomposition Network (BIDeN) to serve as a strong baseline for future work.
Score: 53.760745569495825
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: We present 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 three 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. Code and project page are available.

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