ColorGAN: Automatic Image Colorization with GAN
Yurong Lu, Xianglin Huang, Yan Nan Zhai, Lifang Yang, Yirui Wang · 2023
Colorization has attracted increasing interest in recent years. However, image colorization is an ill-posed problem with multi-modal correct solutions and they still suffer problems of context confusion and object-edge color bleeding. In this paper, we proposed the Color-GAN, a novel auto adversarial learning colorization methods coupled with channel and spatial attention based on residual structure enhanced by feature extractor and skip-connection. Our network learns colorizing in the method of combining perceptual and semantic understanding of color with class distributions. Experimental results show that our network outperformers existing methods on different quality metrics, meanwhile generates state-of-the-art performance on auto image colorization.