To obtain a better shadow removal result on images with complex illumination and texture

we proposed a novel approach based on generative adversarial networks. Firstly

shadow mask is generated by the shadow detection sub-net from input shadow image. Based on this detection result

we proposed an illumination sensitive multi-scale image decomposition method to extract the texture information with less or no illumination information loss. Secondly

shadow matte is generated by the matte generation sub-net for the low scale shadow image to remove shadows in it. Thirdly

the shadow boundary are naturally recovered by the boundary completion sub-net. Finally

the shadow removal result is obtained using a detail recovering method guided by adaptive attenuation factor. Experimental results show that the proposed method can improve the removal performance effectively.