An improved generative adversarial network for remote sensing image denoising
Jing Liu, Xiang PengXia, Zhang XiaoYan · 2021
Existing methods for remote sensing image denoising typically suffer from a common drawback of fuzzy edge information. In this paper, we proposed a Generative Adversarial Network(GAN) based on the residual learning and perceptual loss for image denoising. The proposed GAN is designed with the two parts: The generator network takes the high-frequency layer of noisy image as the input and outputs a clean image after training. In order to eliminate noise better while retaining more edges and details, three residual blocks are embedded in the generator and a perceptual loss function is added to learn the perceptual differences between the denoised images and the ground truth images. The discriminator network based on 70×70 PatchGAN can discern between the denoised image and the clean image through a confidence value. The experiments show that our proposed network achieves superior performances and preserve majority the edge contours and fine details from low-quality observations.