Shadow Removal from a Single Image Using an Enhanced Generative Adversarial Network

Image shadow removal is challenging due to the diversity of shadows and the dependence on the background of the area covered by shadows. In this paper, we design an enhanced generative adversarial network (EGAN) to handle the shadow removal task. EGAN is a complicated generative adversarial network that may exploit previous info, like shadow masks and lightness estimation, for improving recovery capability. In addition, EGAN can effectively utilize the details of shadow regions and eliminate artifacts. EGAN owns three main advantages. First, EGAN employs stricter rules for discriminators, which can upgrade the instability of traditional GAN. Second, EGAN involves a completely unique enhanced attention module, which may efficiently pick up different types of previous info to accelerate the learning efficiency of image reconstruction. Finally, EGAN is essentially an original cascaded network, that is, DRDU, which is beneficial for efficiently detecting hierarchical features of image recovery. Simulation experiments are conducted to verify the effectiveness of the proposed method on two benchmark datasets including SRD and ISTD, the strengths of our proposed method are indicated over the current representative ones according to subjective performance and objective estimation indexes.