Single Image Reflection Removal (SIRR) in real-world images is a challenging
task due to diverse image degradations occurring on the glass surface during
light transmission and reflection. Many existing methods rely on specific prior
assumptions to resolve the problem. In this paper, we propose a general
reflection intensity prior that captures the intensity of the reflection
phenomenon and demonstrate its effectiveness. To learn the reflection intensity
prior, we introduce the Reflection Prior Extraction Network (RPEN). By
segmenting images into regional patches, RPEN learns non-uniform reflection
prior in an image. We propose Prior-based Reflection Removal Network (PRRN)
using a simple transformer U-Net architecture that adapts reflection prior fed
from RPEN. Experimental results on real-world benchmarks demonstrate the
effectiveness of our approach achieving state-of-the-art accuracy in SIRR.

该论文介绍了一种用于解决实际场景中的单幅图像去反射问题的方法，通过提取反射强度先验并结合反射去除网络的设计，在真实世界的基准测试上取得了最先进的准确率。