Single Image Reflection Separation in Scenes with Light Source Only on One Side of the Camera

The phenomenon of reflection superposition manifests itself as complex and prevalent in the real world, leading to the emergence of various simplified linear and non-linear formulas and related models. This study points out the limitations of existing models in scenarios where the light source is only on one side of the camera and conducts a theoretical, quantitative analysis of such scenarios. Based on this, we reconfigured the relevant parameters of the synthetic dataset used in training, compared them with the original settings, and contrasted them with the YTMT model. Experimental results show that the reconfigured parameters improve the model performance on our self-constructed dataset, especially in better understanding the high-level semantics of images.