RSID-CR: Remote Sensing Image Denoising Based on Contrastive Learning

Abstract

In the field of remote sensing image denoising, the current mainstream methods usually only consider using clean or noisy images to guide the network in the training phase. Most of them only apply to specific types of noise, and the denoising effect is not satisfactory enough, with problems such as artifacts and noise residues. In this article, we endeavor to deal with a wide range of noise types, preserving as much detailed information in the image as possible and aiming to address the relevant limitations. Inspired by contrastive learning, we propose a remote sensing image denoising framework based on contrastive learning, named RSID-CR, which constructs positive and negative sample pairs between clean, noisy, and denoised images. Then, we construct a joint loss function consisting of reconstruction loss and contrastive regularization as a guide signal to train the denoising network, such that the denoised image is pushed closer to the clean image and farther away from the noisy image in the feature space. We conduct extensive experiments on two public datasets for five types of noise often present in remote sensing images. In addition, we validate our method using two real noisy remote sensing datasets. The experimental results indicate that our proposed method achieves satisfactory outcomes.