Spatio-Temporal Multi-Image Reflection Removal

Xinxin Zhang, Wenjing Shang, Qiangchang Wang, Yongshun Gong, Qifang Liu

Abstract: In this letter, we propose a precise algorithm to eliminate reflections from two images by utilizing temporal and spatial priors. For the temporal prior, we compute the motion information between reflection layers in the two input reflection-contaminated images. Different from numerous popular multi-image reflection removal methods, our proposed algorithm does not assume that two input images are captured under similar lighting conditions and the same camera settings. Furthermore, the proposed algorithm is robust to the difference between the two reflection layers, such as moving objects and different reflections. For the spatial term, a sparsity gradient regularization is adopted to enforce the spatial smoothness of transmission layers and reflection layers. Importantly, the proposed algorithm does not rely on additional training data or high-performance computing devices. Experimental results on both synthetic images and real-world photographs demonstrate that the proposed algorithm achieves State-of-the-Art performance.