Location-aware Single Image Reflection Removal
This work addresses the problem of removing reflections from single images, which is beneficial for computer vision tasks and photography, offering an incremental improvement over existing methods.
This paper introduces a novel deep-learning method for single image reflection removal that uses a reflection detection module to create a probabilistic confidence map. This map guides the network's feature flow to predict reflection and transmission layers, leading to improved reflection removal results.
This paper proposes a novel location-aware deep-learning-based single image reflection removal method. Our network has a reflection detection module to regress a probabilistic reflection confidence map, taking multi-scale Laplacian features as inputs. This probabilistic map tells if a region is reflection-dominated or transmission-dominated, and it is used as a cue for the network to control the feature flow when predicting the reflection and transmission layers. We design our network as a recurrent network to progressively refine reflection removal results at each iteration. The novelty is that we leverage Laplacian kernel parameters to emphasize the boundaries of strong reflections. It is beneficial to strong reflection detection and substantially improves the quality of reflection removal results. Extensive experiments verify the superior performance of the proposed method over state-of-the-art approaches. Our code and the pre-trained model can be found at https://github.com/zdlarr/Location-aware-SIRR.