Joint image-to-image translation with denoising using enhanced generative adversarial networks - Yan, Lan; Zheng, Wenbo; Wang, Fei-Yue; Gou, Chao (Signal processing. Image communication, 2021)
Impressive progress has been made recently in image-to-image translation using generative adversarial networks (GANs). However, existing methods often fail in translating source images with noise to target domain. To address this problem, we joint image-to-image translation with image denoising and propose an enhanced generative adversarial network (EGAN). In particular, built upon pix2pix, we introduce residual blocks in the generator network to capture deeper multi-level information between source and target image distribution. Moreover, a perceptual loss is proposed to enhance the performance of image-to-image translation. As demonstrated through extensive experiments, our proposed EGAN can alleviate effects of noise in source images, and outperform other state-of-the-art methods significantly. Furthermore, we experimentally indicate that the proposed EGAN is also effective when applied to image denoising. •We study a novel image-to-image translation with noise problem, this is, translating a noisy image in one image domain to a noise-free image in another image domain.•A trainable end-to-end pipeline and a robust GAN-based model named EGAN are proposed to address this problem.•We experimentally demonstrate the effectiveness of the proposed pipeline and the superiority of EGAN compared to a range of GAN-based image-to-image translation methods.