Taking pictures through glass windows almost always produces undesired reflections that degrade the quality of the photo. The ill-posed nature of the reflection removal problem reached the attention of many researchers for more than decades. The main challenge of this problem is the lack of real training data and the necessity of generating realistic synthetic data. In this paper, we proposed a single image reflection removal method based on context understanding modules and adversarial training to efficiently restore the transmission layer without reflection. We also propose a complex data generation model in order to create a large training set with various type of reflections. Our proposed reflection removal method outperforms state-of-the-art methods in terms of PSNR and SSIM on the SIR benchmark dataset.

翻译：通过玻璃窗照相几乎总是会产生不理想的反射,降低照片质量。反射问题不理想的性质引起了许多研究人员数十年来的注意。这一问题的主要挑战是缺乏真正的培训数据和产生现实的合成数据的必要性。在本文中,我们提出了一个单一的图像反射方法,其依据是背景理解模块和对抗性培训,以有效恢复传播层而不进行反射。我们还提议了一个复杂的数据生成模型,以创建包含各种反射的大型培训组。我们提议的反射方法在SIR基准数据集方面优于PSNR和SSIM的最新方法。