Variational Degeneration to Structural Refinement: A Unified Framework for Superimposed Image Decomposition
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Abstract
Decomposing a single mixed image into individual image layers is the common crux of a classical category of tasks in image restoration. Several unified frameworks have been proposed that can handle different types of degradation in superimposed image decomposition. However, there are always undesired structural distortions in the separated images when dealing with complicated degradation patterns. In this paper, we propose a unified framework for superimposed image decomposition that can cope with intricate degradation patterns adaptively. Considering the different mixing patterns between the layers, we introduce a degeneration representation in the latent space to mine the intrinsic relationship between the superimposed image and the degeneration pattern. Moreover, by extracting structure-guided knowledge from the superimposed image, we further propose structural guidance refinement to avoid confusing content caused by structure distortion. Extensive experiments have demonstrated that our method remarkably outperforms other popular image separation frameworks. The method also achieves competitive results on related applications including image deraining, image reflection removal, and image shadow removal, which validates the generalization of the framework.
