D2S-RSG-SSD: Dual Double-Sampling with Random Sub-Samples Generation for Self-Supervised Real Image Denoising.

Abstract

Recent advances in self-supervised image denoising have highlighted the potential of Blind-Spot Networks (BSNs). However, existing methods suffer from three major limitations: (1) Their effectiveness in real-world scenarios is limited by strong assumptions, such as noise independence, which rarely hold in practice. (2) While sampling-based strategies can partially improve performance, BSNs inherently suffer from information loss caused by centroid masking, and removing the blind spot leads to noise overfitting, both of which hinder denoising performance. (3) Sampling-based methods often introduce checkerboard artifacts, yet existing studies typically overlook the fundamental differences between these artifacts and real noise. To address these issues, we propose a novel self-supervised denoising framework, Dual Double-Sampling with Random Sub-samples Generation (D2S-RSG-SSD). To address Limitation 1, we introduce a sampling-based framework that breaks noise dependence by combining Random Sub-samples Generation (RSG) with a cross-paired loss $\mathcal {L}_{RSG}$. RSG generates diverse sub-samples with inherent variance, referred to as sampling differences, which serve as natural perturbations to augment training data and disrupt spatial noise correlations. The proposed loss function ensures full utilization of these sub-samples while stabilizing optimization. To address Limitation 2, we propose a Dual Double-Sampling (D2S) strategy with fixed sampling patterns and a dual-branch architecture. This design reduces reliance on pixel-level information and leverages complementary features to mitigate both noise overfitting and information loss. A key advantage is its compatibility with various advanced denoising networks, lifting the constraint of using BSNs in self-supervised settings. Additionally, we introduce a fixed sub-image sampling strategy to prevent pattern collapse during inference and ensure stability. To address Limitation 3, we explicitly differentiate checkerboard artifacts from real noise and develop a dedicated artifact remover to correct pixel discontinuities caused by sampling-based operations. This design preserves fine image details while reducing over-smoothing. Experiments on benchmark real-noise datasets and self-captured noisy images demonstrate the robustness and generalizability of our framework, achieving better performance over existing methods.