Single Image Reflection Separation by Using Reflection and Refraction Estimations

Tsung-Jung Liu, U.-In Chan, Kuan-Hsien Liu

Abstract: Considering the influence of reflected and transmitted light on physical imaging in real-world scenarios, we propose a method that leverages reflection and refraction coefficients. By calculating these coefficients between the captured image and the transmission image, our approach effectively guides the separation of reflection layers within the captured image. The proposed three-branch framework integrates the Feature Enhancement Module (FEM), which is specifically designed to recover additional details and produce high-quality transmission images. These enhancements significantly improve the performance of the reflection removal process. Comprehensive experiments conducted on various datasets and in comparison with state-of-the-art reflection removal methods demonstrate the effectiveness of our approach. The results show that our method excels in eliminating reflection artifacts and correcting intensity distortions, yielding superior image quality. Especially, the PSNR and SSIM scores of our model outperform existing methods. Furthermore, the simplicity of the input and architecture underscores the practicality of the proposed method. The source code of the proposed method is available at https://reurl.cc/LnAmqx.