Single Image Reflection Removal Based on Dark Channel Sparsity Prior
Xinxin Zhang, Kaixin Xing, Qifang Liu, Da Chen, Yilong Yin · IEEE Transactions on Circuits and Systems for Video Technology · 2023
The major task of reflection removal methods is to restore a reflection-free image from a reflection-contaminated image taken through glass. We propose an algorithm to remove reflections from a single image by means of the$l_{0}$-regularized dark channel sparsity prior and an$l_{0}$gradient sparsity prior. In addition, we analyze the difference between the dark channel map in the reflection-contaminated image and the reflection-free image empirically and mathematically. Moreover, a new data fidelity term is introduced to handle strong reflections and preserve high-frequency details in the recovered transmission image. Different from the model used in most state-of-the-art methods, our reflection removal model does not rely on the assumption of out-of-focus objects in the reflection layer. Quantitative evaluation on several publicly available real-world image datasets including ground-truth demonstrates the high accuracy of our algorithm. Qualitative evaluation of extensive experimental results on real-world images shows the competitive performance of the proposed method compared with the state-of-the-art reflection removal methods.