Random Sub-Samples Generation for Self-Supervised Real Image Denoising

TL;DR

This paper introduces a novel self-supervised denoising framework called SDAP that uses random sub-sample generation and perturbation techniques to improve real image denoising performance, outperforming existing methods.

Contribution

The paper proposes a new RSG strategy combined with a cyclic sample difference loss to enhance BSN for real noise, addressing limitations of previous self-supervised methods.

Findings

Significantly outperforms state-of-the-art self-supervised denoising methods on real datasets.

Adding perturbations via RSG improves BSN performance on real noise.

The proposed framework effectively handles real-world noise without paired training data.

Abstract

With sufficient paired training samples, the supervised deep learning methods have attracted much attention in image denoising because of their superior performance. However, it is still very challenging to widely utilize the supervised methods in real cases due to the lack of paired noisy-clean images. Meanwhile, most self-supervised denoising methods are ineffective as well when applied to the real-world denoising tasks because of their strict assumptions in applications. For example, as a typical method for self-supervised denoising, the original blind spot network (BSN) assumes that the noise is pixel-wise independent, which is much different from the real cases. To solve this problem, we propose a novel self-supervised real image denoising framework named Sampling Difference As Perturbation (SDAP) based on Random Sub-samples Generation (RSG) with a cyclic sample difference loss. Specifically, we dig deeper into the properties of BSN to make it more suitable for real noise. Surprisingly, we find that adding an appropriate perturbation to the training images can effectively improve the performance of BSN. Further, we propose that the sampling difference can be considered as perturbation to achieve better results. Finally we propose a new BSN framework in combination with our RSG strategy. The results show that it significantly outperforms other state-of-the-art self-supervised denoising methods on real-world datasets. The code is available at https://github.com/p1y2z3/SDAP.