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 $\mathcal {L}_{RSG}$LRSG. 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.

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.

Deep learning-based methods have dominated the field of image denoising with their superior performance. Most of them belong to the non-blind denoising approaches assuming that the noise is known at a specific level. However, real-world noise is complex and usually unknown. Since the distribution and level of noise are often unavailable, it will lead to severe performance degradation for non-blind denoising methods. Therefore, introducing noise levels is crucial for the challenging real-world denoising problem. Meanwhile, we observe that noise level mismatch will bring some artifacts to the denoised images. An intuitive solution is using the intermediate denoised images to correct the inaccurate noise level maps. Thus, we introduce an iterative correction scheme, yielding better results than direct noise prediction. We further propose an effective guided feature domain denoising residual network that can promote denoising for various real-world noises using iteratively denoised features, initial image features, and noise level maps. Experimental results on real-world image datasets show that the proposed method can provide excellent visual and objective performance for the real-world denoising task.

Recently, the popularity of deep learning has brought broad applications of computer vision technology in industrial information systems. However, the process of image acquisition will inevitably introduce noise, which may heavily degrade image visual quality. Most of the proposed denoising methods are nonblind and they have limited performance in removing real noise with different noise levels. To overcome this problem, we propose a deep convolutional neural network (CNN)-based blind model, i.e., channel affine self-attention (CASA) based progressively updated network (CasaPuNet) for real image denoising. First, we introduce degradation mapping module (DMM) to extract degradation information, which makes the remaining subnetwork of CasaPuNet perform nonblind denoising. Then, CasaPuNet adopts a multistage architecture, which resolves the large gap between the noisy input and clean output into several small gaps and eliminates these small gaps step by step through progressive inference. Finally, a novel CASA is designed to adaptively fuse the features from multiple stages according to input statistics. Specifically, CASA extracts channel information from different features and converts them into channel weights through an affine structure for adaptive adjustment. CASA brings a significant performance gain with a small number of parameters. Extensive experiments demonstrate that CasaPuNet outperforms state-of-the-art denoising methods both quantitatively and visually.

Motivated by the achievements in model-based methods and the advances in deep networks, we propose a novel enhanced latent space blind model based deep unfolding network, namely ScaoedNet, for complex real image denoising. It is derived by introducing latent space, noise information, and guidance constraint into the denoising cost function. A self-correction alternative optimization algorithm is proposed to split the novel cost function into three alternative subproblems, i.e., guidance representation (GR), degradation estimation (DE) and reconstruction (RE) subproblems. Finally, we implement the optimization process by a deep unfolding network consisting of GR, DE and RE networks. For higher performance of the DE network, a novel parameter-free noise feature adaptive enhancement (NFAE) layer is proposed. To synchronously and dynamically realize internal-external feature information mining in the RE network, a novel feature multi-modulation attention (FM2A) module is proposed. Our approach thereby leverages the advantages of deep learning, while also benefiting from the principled denoising provided by the classical model-based formulation. To the best of our knowledge, our enhanced latent space blind model, optimization scheme, NFAE and FM2A have not been reported in the previous literature. Experimental results show the promising performance of ScaoedNet on real image denoising. Code is available at https://github.com/chaoren88/ScaoedNet.