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.
翻译:我们提议并研究一项名为“盲人图像分解”的新颖任务,它要求将一个叠加图像分离成在盲人环境中的成份基本图像,即混合和混合机制所涉及的源组成部分是未知的。例如,雨水可能由多个组成部分组成,例如雨量、雨滴、雪和烟雾。雨量图像可以作为这些组成部分的任意组合处理,其中一些或所有组成部分。如何将叠加图像分解成不同的源组成部分,如雨量图像,是走向现实世界视觉系统的关键一步。为了便利对这一新任务的研究,我们建造了多个基准数据集,包括跨多个领域的混合图像分解、真实的图像分解以及影子/反光、联合的影子/反光/水标记去除。此外,我们提议建立一个简单而普遍的盲人图像分解网(BIDeN),作为未来工作的坚实基线。实验结果表明我们基准的可靠性和BIDN的有效性。