Shadow Removal from Images Using Conditional GANs

Abstract

Shadow removal has many applications in computer vision and shadow-free images have better visual quality. In recent studies, deep learning-based CNN models have shown better performance than traditional approaches to shadow removal. GAN takes the advantage of two independent neural networks. This study about shadow removal is implemented using GAN. Shadow removal is divided into two tasks: detection and removal. The two sub-networks stacked upon each other are based on conditional GAN. The input shadow image 256*256 is fed to the first generator network to produce a shadow mask, which is input to the second generator network along with a shadow image to obtain a shadow-free image.