Scholarlink

<p>U-Net-based deep learning models have garnered significant attention in recent years due to their strong denoising capabilities in image restoration tasks. This study critically evaluates both the strengths and limitations of these models, with a particular focus on their architectural design and constituent components, in an effort to further advance denoising performance. Based on the insights derived from these analyses, a novel architecture–termed U-Tunnel-Net–is proposed. The model is trained on the UNS and Waterloo datasets, each augmented with Rayleigh-distributed speckle noise at four distinct intensity levels (<InlineEquation ID="IEq1"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="41598_2025_10220_Article_IEq1.gif" Format="GIF" Height="10" Rendition="HTML" Resolution="72" Type="Linedraw" Width="13" /> </InlineMediaObject> <EquationSource Format="TEX">\(\sigma\)</EquationSource> </InlineEquation> = 0.10, 0.25, 0.50, and 0.75), and evaluated on the UNS, BSD68, and Set12 datasets. Performance assessment is conducted using peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and runtime as evaluation metrics. Leveraging a novel network design strategy and a newly introduced convolutional block, U-Tunnel-Net consistently achieves superior performance compared to other U-Net-based denoising models. A key architectural innovation lies in the repositioning of the pooling operation within the Tunnel Blocks, which differentiates the proposed model from conventional U-Net variants. Experimental results on both benchmark and real-world datasets confirm that U-Tunnel-Net outperforms several state-of-the-art denoising methods.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

An effective image despeckling and reconstruction approach using U-Net based model and comparative analysis

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年9月30日

查看全文
  • M. S. Gokmen,
  • Bilgehan Arslan,
  • C. Bumgardner,
  • A. Imran

摘要

U-Net-based deep learning models have garnered significant attention in recent years due to their strong denoising capabilities in image restoration tasks. This study critically evaluates both the strengths and limitations of these models, with a particular focus on their architectural design and constituent components, in an effort to further advance denoising performance. Based on the insights derived from these analyses, a novel architecture–termed U-Tunnel-Net–is proposed. The model is trained on the UNS and Waterloo datasets, each augmented with Rayleigh-distributed speckle noise at four distinct intensity levels ( \(\sigma\) = 0.10, 0.25, 0.50, and 0.75), and evaluated on the UNS, BSD68, and Set12 datasets. Performance assessment is conducted using peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and runtime as evaluation metrics. Leveraging a novel network design strategy and a newly introduced convolutional block, U-Tunnel-Net consistently achieves superior performance compared to other U-Net-based denoising models. A key architectural innovation lies in the repositioning of the pooling operation within the Tunnel Blocks, which differentiates the proposed model from conventional U-Net variants. Experimental results on both benchmark and real-world datasets confirm that U-Tunnel-Net outperforms several state-of-the-art denoising methods.