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<p>In recent years, Convolutional Neural Networks (CNNs) have achieved remarkable success in various computer vision tasks, including image denoising. Image denoising focuses on reconstructing a clean image from its noise-corrupted counterpart. In this paper, we propose BIRUNet, a bilateral-filter-based noise-residue U-Net enhanced with gradient estimation. The objective of this research is to improve the learning capability of the traditional U-Net by integrating manually derived image priors. Although several improved U-Net variants exist, many suffer from high computational cost and rely solely on learned noise patterns, which limits their reconstruction quality. To address these issues, BIRUNet incorporates two additional priors: (i) noise residue extracted using a traditional bilateral filter, and (ii) gradient information derived from the input image. These priors are concatenated with the noisy grayscale image and fed into an encoder-decoder U-Net architecture to generate a more accurate denoised output. The proposed model is evaluated both quantitatively and visually across multiple datasets. With a particular focus on preserving edge details, SSIM values are compared against those of more complex models, demonstrating superior performance. BIRUNet achieves a PSNR of 26.66 dB at a high noise level (σ = 50), confirming its effectiveness in challenging denoising scenarios.</p>

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Enhancing U-Net for image denoising with bilateral filter noise residue and gradient estimation (BIRUNet)

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月7日

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  • S. Soniya,
  • K. C. Sriharipriya,
  • J. Christopher Clement,
  • Umashankar Subramaniam

摘要

In recent years, Convolutional Neural Networks (CNNs) have achieved remarkable success in various computer vision tasks, including image denoising. Image denoising focuses on reconstructing a clean image from its noise-corrupted counterpart. In this paper, we propose BIRUNet, a bilateral-filter-based noise-residue U-Net enhanced with gradient estimation. The objective of this research is to improve the learning capability of the traditional U-Net by integrating manually derived image priors. Although several improved U-Net variants exist, many suffer from high computational cost and rely solely on learned noise patterns, which limits their reconstruction quality. To address these issues, BIRUNet incorporates two additional priors: (i) noise residue extracted using a traditional bilateral filter, and (ii) gradient information derived from the input image. These priors are concatenated with the noisy grayscale image and fed into an encoder-decoder U-Net architecture to generate a more accurate denoised output. The proposed model is evaluated both quantitatively and visually across multiple datasets. With a particular focus on preserving edge details, SSIM values are compared against those of more complex models, demonstrating superior performance. BIRUNet achieves a PSNR of 26.66 dB at a high noise level (σ = 50), confirming its effectiveness in challenging denoising scenarios.