Separate from Observation: Unsupervised Single Image Layer Separation

Unsupervised single image layer separation aims at extracting two layers from an input image where these layers follow different distributions. This problem arises most notably in reflection inference removal and intrinsic image decomposition. Since there exist an infinite set of combinations that can construct the given input image, one could infer nothing about the solutions without additional assumptions. To address the problem, we make the shared information consistency assumption and separated layer independence assumption to constrain the solutions. In this end, we propose an unsupervised single image separation framework based on cycle GANs and self-supervised learning. The proposed framework is applied for the reflection removal and intrinsic image problems. Numerical and visual results show that the proposed method achieves the state-of-the-art performance among unsupervised methods which require single image as input. Based on the slightly modified version of the presented framework, we also demonstrate the promising results of decomposing an image into three layer.