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.

Recent researches have shown that the success of Transformers comes from their macro-level framework and advanced components, not just their self-attention (SA) mechanism. Comparable results can be obtained by replacing SA with spatial pooling, shifting, MLP, fourier transform and constant matrix, all of which have spatial information encoding capability like SA. In light of these findings, this work focuses on combining efficient spatial information encoding technology with superior macro architectures in Transformers. We rethink spatial convolution to achieve more efficient encoding of spatial features and dynamic modulation value representations by convolutional modulation techniques. The large-kernel convolution and Hadamard product are utilizated in the proposed Multi-orders Long-range convolutional modulation (MOLRCM) layer to imitate the implementation of SA. Moreover, MOLRCM layer also achieve long-range correlations and self-adaptation behavior, similar to SA, with linear complexity. On the other hand, we also address the sub-optimality of vanilla feed-forward networks (FFN) by introducing spatial awareness and locality, improving feature diversity, and regulating information flow between layers in the proposed Spatial Awareness Dynamic Feature Flow Modulation (SADFFM) layer. Experiment results show that our proposed efficient information modulation network (EIMN) performs better both quantitatively and qualitatively. Codes and supplementary materials link: https://github.com/liux520/EIMN.

In this work, we propose PMSDSEN, a parallel multi-scale encoder-decoder network architecture for semantic segmentation, inspired by the human visual perception system's ability to aggregate contextual information in various contexts and scales. Our approach introduces the efficient Parallel Multi-Scale Detail and Semantic Encoding (PMSDSE) unit to extract detailed local information and coarse large-range relationships in parallel, enabling the recognition of object boundaries and object-level areas. By stacking multiple PMSDSEs, our network learns fine-grained details and textures along with abstract category and semantic information, effectively utilizing a larger range of surrounding context information for robust segmentation. To further enhance the network's receptive field without increasing computational complexity, the Multi-Scale Semantic Extractor (MSSE) at the end of the encoder is utilized for multi-scale semantic context extraction and detailed information encoding. Additionally, the Dynamic Weighted Feature Fusion (DWFF) strategy is employed to integrate shallow layer detail information and deep layer semantic information during the decoder stage. Our method can obtain multi-scale context from local to global, achieving efficiently low-level feature extraction to high-level semantic interpretation at different scales and in different contexts. Without bells and whistles, PMSDSEN obtains a better trade-off between accuracy and complexity on popular benchmarks, including Cityscapes and Camvid. Specifically, PMSDSEN attains 73.2% mIoU with only 0.9M parameters on the Cityscapes test set. Codes and supplementary materials link: https://github.com/liux520/PMSDSEN.