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Polarized Reflection Removal with Perfect Alignment in the Wild
We present a novel formulation to removing reflection from polarized ima...
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Face Image Reflection Removal
Face images captured through the glass are usually contaminated by refle...
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Semantic Guided Single Image Reflection Removal
Reflection is common in images capturing scenes behind a glass window, w...
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Location-aware Single Image Reflection Removal
This paper proposes a novel location-aware deep learning-based single im...
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Deep-Masking Generative Network: A Unified Framework for Background Restoration from Superimposed Images
Restoring the clean background from the superimposed images containing a...
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Separating Reflection and Transmission Images in the Wild
The reflections caused by common semi-reflectors, such as glass windows,...
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Fast Single Image Reflection Suppression via Convex Optimization
Removing undesired reflections from images taken through the glass is of...
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CRRN: Multi-Scale Guided Concurrent Reflection Removal Network
Removing the undesired reflections from images taken through the glass is of broad application to various computer vision tasks. Non-learning based methods utilize different handcrafted priors such as the separable sparse gradients caused by different levels of blurs, which often fail due to their limited description capability to the properties of real-world reflections. In this paper, we propose the Concurrent Reflection Removal Network (CRRN) to tackle this problem in a unified framework. Our proposed network integrates image appearance information and multi-scale gradient information with human perception inspired loss function, and is trained on a new dataset with 3250 reflection images taken under diverse real-world scenes. Extensive experiments on a public benchmark dataset show that the proposed method performs favorably against state-of-the-art methods.
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