Scholarlink

<p>During spacecraft launch, flight, and docking operations, monitored images often suffer from spatially variant blur caused by atmospheric turbulence, defocusing, and relative motion. To address this challenge, we propose a novel unsupervised deblurring framework tailored specifically for spacecraft imagery. Our approach incorporates three key innovations: First, we design a detail-preserving local region selection strategy based on multi-scale morphological gradients with adaptive thresholding, which optimizes regions for blur kernel estimation. Second, we define a blur kernel error term and integrate it into the degradation model, introducing explicit error correction constraints into the alternating iterative minimization process. Third, we incorporate Shearlet transform regularization to enhance the recovery of fine local details. Experimental results demonstrate that our method significantly outperforms state-of-the-art unsupervised techniques and even surpasses several advanced deep learning approaches in preserving complex structural details under spatially variant degradation. Our code and data are available at <a href="https://github.com/bsfsf/Image_deblur">https://github.com/bsfsf/Image_deblur</a>.</p>

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Enhanced space-variant deblurring of spacecraft images via detail-preserving techniques

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月15日

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  • Hanyu Hong,
  • Shuai Guo,
  • Zhiwen Liu,
  • Nong Sang,
  • Hanyu Wang,
  • Dapeng Tian,
  • Liang Ye

摘要

During spacecraft launch, flight, and docking operations, monitored images often suffer from spatially variant blur caused by atmospheric turbulence, defocusing, and relative motion. To address this challenge, we propose a novel unsupervised deblurring framework tailored specifically for spacecraft imagery. Our approach incorporates three key innovations: First, we design a detail-preserving local region selection strategy based on multi-scale morphological gradients with adaptive thresholding, which optimizes regions for blur kernel estimation. Second, we define a blur kernel error term and integrate it into the degradation model, introducing explicit error correction constraints into the alternating iterative minimization process. Third, we incorporate Shearlet transform regularization to enhance the recovery of fine local details. Experimental results demonstrate that our method significantly outperforms state-of-the-art unsupervised techniques and even surpasses several advanced deep learning approaches in preserving complex structural details under spatially variant degradation. Our code and data are available at https://github.com/bsfsf/Image_deblur.