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<p>In recent years, compressive sensing (CS) theory has garnered significant attention due to its advantages in high-resolution image processing. However, high-resolution images are susceptible to noise contamination prior to sampling, which leads to a noise folding phenomenon during CS denoising and reconstruction. This issue severely affects the visual quality of the reconstructed images. To address this issue, this paper proposes a Compressive Sensing Transformer Unfolding Network (CST-UNet) that leverages image degradation priors for high-quality image reconstruction. Specifically, we first design a Degradation Prior Gradient Descent (DPGD) module to learn noise degradation and guide adaptive gradient descent. Next, we develop a Dual-Path Transformer-CNN (DPTC) hybrid framework to capture both local and global contextual information, thereby mitigating block artifacts. Finally, we introduce inter-stage feature cross-attention (ISFCA) blocks to enhance information interaction between stages. Extensive experimental results demonstrate that the proposed CST-UNet achieves high visual quality for reconstructed images, even under conditions of noise pollution and low sampling rates. Codes are available at <a href="https://github.com/xiaoludiver/HT-CUNet">https://github.com/xiaoludiver/CST-UNet</a></p>

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Compressed sensing transformer unfolding network for high resolution image denoising

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年6月13日

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  • Jie Zhang,
  • Wenxiao Huang,
  • Miaoxin Lu,
  • Linwei Li,
  • Yongpeng Shen,
  • Yanfeng Wang,
  • Jinsong Du

摘要

In recent years, compressive sensing (CS) theory has garnered significant attention due to its advantages in high-resolution image processing. However, high-resolution images are susceptible to noise contamination prior to sampling, which leads to a noise folding phenomenon during CS denoising and reconstruction. This issue severely affects the visual quality of the reconstructed images. To address this issue, this paper proposes a Compressive Sensing Transformer Unfolding Network (CST-UNet) that leverages image degradation priors for high-quality image reconstruction. Specifically, we first design a Degradation Prior Gradient Descent (DPGD) module to learn noise degradation and guide adaptive gradient descent. Next, we develop a Dual-Path Transformer-CNN (DPTC) hybrid framework to capture both local and global contextual information, thereby mitigating block artifacts. Finally, we introduce inter-stage feature cross-attention (ISFCA) blocks to enhance information interaction between stages. Extensive experimental results demonstrate that the proposed CST-UNet achieves high visual quality for reconstructed images, even under conditions of noise pollution and low sampling rates. Codes are available at https://github.com/xiaoludiver/CST-UNet