Reflection Removal via Residual Space Selection Mechanism Guided by Reflection-Free Cues

With the rapid development of computer vision technology, images have become one of the key tools for people to record and understand the world. However, image quality often suffers from various external factors, such as the reflections caused when shooting through transparent media like glass. These issues not only degrade image quality but also negatively impact subsequent image processing tasks. To address this, this paper proposes a reflection removal method based on a reflection-free cue and a residual space selection mechanism. This model effectively avoids introducing flash artifacts while also incorporating an alignment mechanism to address the multi-image misalignment problem, thereby mitigating the occurrence of ghosting and blurring artifacts. Experimental results demonstrate that this method performs exceptionally well on real image datasets, outpacing current mainstream image reflection removal models. Additionally, compared to the state-of-the-art flash index methods, our approach achieves an improvement of nearly 3.33% in PSNR, about 4.17% in LPIPS, and an increase of 0.03 in SSIM, which represents a certain degree of enhancement compared to the original value. By introducing reflection-free cues and a residual space selection mechanism, the findings of this study provide a new solution for reflection removal in real-world scenarios, holding significant theoretical and practical value.