Intensity-aware GAN for Single Image Reflection Removal

Single image reflection removal is a challenging task in computer vision. Most existing approaches rely on carefully handcrafted priors to solve the problem. Contrast to the optimization-based methods, an intensity-aware GAN with dual generators is proposed to directly estimate the function which transforms the mixture image into the reflection image itself. From the observation that the reflection layer has more discriminating power in the region with low intensity than that in the region with high intensity, the proposed architecture better describes the characteristic of the model. Moreover, a reflection image synthesis method based on the screen blending model is also presented. Experimental results demonstrate that the results of reflection removal are satisfactory in real cases while comparing with state-of-the-art methods.