Single Image Reflection Removal with Reflection Intensity Prior
  Knowledge
Dongshen Han Seungkyu Lee Chaoning Zhang Heechan Yoon Hyukmin Kwon HyunCheol Kim HyonGon Choo
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.