An improved generative adversarial network for remote sensing image denoising

Jing Liu; Pengxia Xiang; Xiaoyan Zhang

doi:10.1117/12.2599751

30 June 2021 An improved generative adversarial network for remote sensing image denoising

Jing Liu, Pengxia Xiang, Xiaoyan Zhang

Proceedings Volume 11878, Thirteenth International Conference on Digital Image Processing (ICDIP 2021); 1187811 (2021) https://doi.org/10.1117/12.2599751
Event: Thirteenth International Conference on Digital Image Processing, 2021, Singapore, Singapore

Abstract

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.

Citation Download Citation

Jing Liu, Pengxia Xiang, and Xiaoyan Zhang "An improved generative adversarial network for remote sensing image denoising", Proc. SPIE 11878, Thirteenth International Conference on Digital Image Processing (ICDIP 2021), 1187811 (30 June 2021); https://doi.org/10.1117/12.2599751

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