联合块匹配与稀疏表示的卫星云图修复

金炜; 王文龙; 符冉迪; 田文哲; 尹曹谦

doi:10.3788/OPE.20142207.1886

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联合块匹配与稀疏表示的卫星云图修复

信息科学 | 更新时间：2020-08-13

- 联合块匹配与稀疏表示的卫星云图修复
- Satellite cloud image inpainting based on patch matching and sparse representation
- 光学精密工程 2014年22卷第7期页码：1886-1895
- 作者机构：
  
  宁波大学信息科学与工程学院,浙江宁波,315211
- 作者简介：
- 基金信息：
  
  国家自然科学基金资助项目（No.61271399，No.61373068）();浙江省自然科学基金资助项目（No.Y1111061）();宁波市自然科学基金资助项目（N
- DOI：10.3788/OPE.20142207.1886
  中图分类号： TP75
- 收稿日期：2013-10-10，
  
  修回日期：2013-11-29，
  
  纸质出版日期：2014-07-25
- 稿件说明：
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金炜, 王文龙, 符冉迪等. 联合块匹配与稀疏表示的卫星云图修复[J]. 光学精密工程, 2014,22(7): 1886-1895

JIN Wei, WANG Wen-long, FU Ran-di etc. Satellite cloud image inpainting based on patch matching and sparse representation[J]. Editorial Office of Optics and Precision Engineering, 2014,22(7): 1886-1895
金炜, 王文龙, 符冉迪等. 联合块匹配与稀疏表示的卫星云图修复[J]. 光学精密工程, 2014,22(7): 1886-1895 DOI： 10.3788/OPE.20142207.1886.

JIN Wei, WANG Wen-long, FU Ran-di etc. Satellite cloud image inpainting based on patch matching and sparse representation[J]. Editorial Office of Optics and Precision Engineering, 2014,22(7): 1886-1895 DOI： 10.3788/OPE.20142207.1886.

摘要

针对卫星云图在接收及传输过程中受噪声、大气湍流、太阳风暴及卫星轨道漂移等影响造成的云图数据破损，提出了一种联合块匹配与稀疏表示的卫星云图修复方法。首先，根据破损区域的优先权值确定待修复像素，对该像素的邻域进行分块处理。然后，利用待修复块与各匹配块之间的结构相似度，建立相应的冗余字典；通过求解稀疏表示问题修复该破损区域。最后，沿着等照度线不断更新优先权值，实现整幅图像的修复。实验结果表明，提出的方法不仅能避免传统偏微分方程（PDE）修复法所导致的结构丢失，也能很好地改善基于纹理填充修复方法所导致的修复不足及块效应现象。测试结果显示：在云图存在局部区域缺失时，修复后云图的峰值信噪比（PSNR）比匹配追踪法及总变分法的修复结果平均提高了8.50 dB和0.28 dB，而且在纹理细节及边缘区域具有更好的视觉效果。

Abstract

For some defects of satellite cloud images caused by noises

atmospheric turbulence

solar storms and satellite orbit drifts in the receiving and transmission process

a novel satellite cloud image inpainting method using patch matching and sparse representation was proposed.Firstly

a pixel to be inpainted was searched according to the priority of the damaged area.The neighborhood of this pixel was divided into patches

and a redundant dictionary was constructed by calculating the structural similarity of inpainting patch and matching patches.Then the damaged area was repaired by solving a sparse representation problem.Finally

the whole cloud image was inpainted by updating priority along an isophote repeatedly.The experimental results show that the proposed method can not only avoid the structure missing from the traditional Partial Differential Equation (PDE) method

but also can improve the texture details and blocking effect of the texture filling repair method.For the cloud image with local defects

the inpainted image by proposed method can improve Peak Signal to Noise Ratio(PSNR) by 8.50 dB and 0.28 dB as compared to Matching Pursuit (MP) method and Total Variation (TV) method respectively.It shows better visual effects on texture details and edge regions.

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references

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