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FIPHN: Feature-Integrated Patch-Hierarchical Network for Single Image Reflection Removal

Single image reflection removal is a challenging task because important features will be completely obstructed by strong reflections. During removing strong reflections, existing methods focus on mining global contextual features, which will lose some useful local features and fail to remove strong reflections completely. To solve this problem, we propose a feature-integrated patch-hierarchical network (FIPHN), which progressively removes reflections by focusing on integrating global and local features. Specifically, we design a layer-wise feature integration module (LFIM) to integrate global and local features layer by layer across multi-scale stages, effectively enhancing the representation ability of features. Moreover, we design a patch-wise feature integration module (PFIM) to extract contextual features between adjacent patches, avoiding the loss of important features. Meanwhile, with the guidance of ground-truth images, PFIM provides intermediate supervision signals to promote the subnetwork training at each stage. Experimental results show the superiority of our method compared with existing methods.