Abstract: Although the existing learning-based shadow removal methods have made some progress,these methods mainly focus on the image itself and do not well explore other information related to shadow.These methods often result in problems such as image texture blurring or illumination inconsistency.To solve these problems,this paper proposes a new network based on genera-tive adversarial network (GAN) to remove shadows in the image.First,it uses an encoder-decoder to obtain a coarse shadow removal result.Then,it optimizes the coarse result by utilizing the shadow-related residual information to produce more realistic and natural shadow removal image.Our generator contains three encoder-decoder structures.The first encoder-decoder is used to restore the illumination of the image and generate a coarse shadow removal result.To solve the problem of color and illumination inconsistency,the residual information is input into the following encoder-decoder to correct the coarse results.Furthermore,the third encoder-decoder is used to refine the details in the image,which can avoid texture inconsistency between shadow regions and nonshadow regions.The discriminator is used to identify the authenticity of the image shadow removal result.Experiments show that the proposed method achieves the best RMSE value in both shaded region and non-shaded region,and effectively solves the problem of texture blur.In addition,the shadow removal image generated by the proposed method is much closer to the real image,which proves the effectiveness and feasibility of the proposed method.

Key words: Encoding-decoding structure, Generative Adversarial Network, Patch discrimi-nator, Residual information, Shadow removal