CRRN: Multi-Scale Guided Concurrent Reflection Removal Network
This addresses the issue of reflection removal for computer vision applications, but it appears incremental as it builds on existing learning-based approaches with a new network architecture and dataset.
The paper tackles the problem of removing undesired reflections from images taken through glass by proposing the Concurrent Reflection Removal Network (CRRN), which integrates appearance and gradient information with a human perception loss, and it performs favorably against state-of-the-art methods on a public benchmark dataset.
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.