Removing the undesired reflections of images taken through glass is an important problem in digital photography and many other vision applications. The so-called ghosting effect, i.e., the pattern repetitiveness in reflection, is an effective cue used by existing techniques to remove reflection from images. Existing methods take a two-stage approach that first estimates the parameters of ghosting effect and then models reflection removal as a two-layer separation problem: reflection layer and latent image layer. This paper aims at addressing one main challenge in such an approach, i.e., how to distinguish the repetitive patterns on the later image layer and the ghosting patterns on the reflection layer. Based on the observation that the number of repeats of natural image patterns is often different from that of ghosting patterns, we propose a wavelet transform based regularization method. Together with a novel weighting scheme, the proposed method is capable of accurately separating two layers, and experimental results justified its advantages over the existing ones on both synthetic and real data set.

中文翻译：

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从具有重影效果的单个图像中去除反射

去除通过玻璃拍摄的图像的不需要的反射是数码摄影和许多其他视觉应用中的一个重要问题。所谓的重影效应，即反射中的图案重复性，是现有技术用来从图像中去除反射的有效线索。现有方法采用两阶段方法，首先估计重影效应的参数，然后将反射去除建模为两层分离问题：反射层和潜像层。本文旨在解决这种方法中的一个主要挑战，即如何区分后面图像层上的重复图案和反射层上的重影图案。基于观察到自然图像图案的重复次数往往与重影图案的重复次数不同，我们提出了一种基于小波变换的正则化方法。与新的加权方案一起，所提出的方法能够准确地分离两层，并且实验结果证明了其在合成和真实数据集上优于现有方法的优势。