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<p>Image denoising is a fundamental low-level vision task essential for restoring perceptual quality and improving downstream visual performance. Despite significant progress in deep learning-based approaches, existing methods often fail to balance noise suppression and structural detail preservation under complex noise distributions, highlighting the need for better exploitation of spatial feature dependencies within images. To tackle these issues, this article proposes SPCANet, a novel denoising framework incorporating gradient information through three specialized attention mechanisms: Pixel Attention module, Channel Attention Module, and Spatial Feature Attention Block. SPCANet effectively utilizes gradients at multiple feature processing stages to enhance image features and reduce noise. Its comprehensive approach simultaneously exploits pixel-level, channel-wise, and spatial feature dependencies, enabling robust noise reduction across various image structures. Extensive experiments are conducted on standard benchmark datasets - <i>BSD68/CBSD68</i>, <i>SunHays80</i>, and <i>Set5/Set12</i> - using Gaussian noise levels of 30, 40, 50 and 60. Quantitative comparisons with multiple state-of-the-art methods, including DnCNN, MemNet, and SwinIR, demonstrate the superior performance of SPCANet, achieving an average PSNR gain of 0.93 dB and SSIM improvement of 0.111 across all datasets. Qualitative results further show that SPCANet preserves fine structures and edges more effectively than competing methods. These results confirm the robustness and generalization capability of SPCANet, providing a comprehensive and efficient solution to the general image denoising problem.</p>

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SPCANet: spatial, pixel and channel attention guided deep image denoiser

  • 期刊论文

  • 发表日期：

    2026年2月10日

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  • Anirban Saha,
  • Debashis Das,
  • Suman Kumar Maji

摘要

Image denoising is a fundamental low-level vision task essential for restoring perceptual quality and improving downstream visual performance. Despite significant progress in deep learning-based approaches, existing methods often fail to balance noise suppression and structural detail preservation under complex noise distributions, highlighting the need for better exploitation of spatial feature dependencies within images. To tackle these issues, this article proposes SPCANet, a novel denoising framework incorporating gradient information through three specialized attention mechanisms: Pixel Attention module, Channel Attention Module, and Spatial Feature Attention Block. SPCANet effectively utilizes gradients at multiple feature processing stages to enhance image features and reduce noise. Its comprehensive approach simultaneously exploits pixel-level, channel-wise, and spatial feature dependencies, enabling robust noise reduction across various image structures. Extensive experiments are conducted on standard benchmark datasets - BSD68/CBSD68, SunHays80, and Set5/Set12 - using Gaussian noise levels of 30, 40, 50 and 60. Quantitative comparisons with multiple state-of-the-art methods, including DnCNN, MemNet, and SwinIR, demonstrate the superior performance of SPCANet, achieving an average PSNR gain of 0.93 dB and SSIM improvement of 0.111 across all datasets. Qualitative results further show that SPCANet preserves fine structures and edges more effectively than competing methods. These results confirm the robustness and generalization capability of SPCANet, providing a comprehensive and efficient solution to the general image denoising problem.