基于归一化超拉普拉斯先验项的运动模糊图像盲复原

王国栋; 徐洁; 潘辰宽; 刘存良; 杨金宝

doi:10.3788/OPE.20132105.1340

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基于归一化超拉普拉斯先验项的运动模糊图像盲复原

信息科学 | 更新时间：2020-08-12

- 基于归一化超拉普拉斯先验项的运动模糊图像盲复原
- Blind image restoration based on normalized hyper laplacian prior term
- 光学精密工程 2013年21卷第5期页码：1340-1348
- 作者机构：
  
  青岛大学信息工程学院2. 青岛大学物理科学学院
- 作者简介：
- 基金信息：
  
  多相图像分割的全局凸优化变分模型及其快速算法();复杂运动模糊退化图像的盲复原关键技术研究()
- DOI：10.3788/OPE.20132105.1340
  中图分类号： TP394.1
- 收稿日期：2012-11-26，
  
  修回日期：2013-01-14，
  
  网络出版日期：2013-05-24，
  
  纸质出版日期：2013-05-15
- 稿件说明：
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王国栋徐洁潘辰宽刘存良杨金宝. 基于归一化超拉普拉斯先验项的运动模糊图像盲复原[J]. 光学精密工程, 2013,21(5): 1340-1348

WANG Guo-Dong XU Jie PAN Chen-kuan LIU Cun-liang YANG Jin-bao. Blind image restoration based on normalized hyper laplacian prior term[J]. Editorial Office of Optics and Precision Engineering, 2013,21(5): 1340-1348
王国栋徐洁潘辰宽刘存良杨金宝. 基于归一化超拉普拉斯先验项的运动模糊图像盲复原[J]. 光学精密工程, 2013,21(5): 1340-1348 DOI： 10.3788/OPE.20132105.1340.

WANG Guo-Dong XU Jie PAN Chen-kuan LIU Cun-liang YANG Jin-bao. Blind image restoration based on normalized hyper laplacian prior term[J]. Editorial Office of Optics and Precision Engineering, 2013,21(5): 1340-1348 DOI： 10.3788/OPE.20132105.1340.

摘要

基于变分方法提出了一种运动模糊退化图像的盲复原算法。考虑自然场景的图像梯度符合长拖尾概率分布，提出的方法采用归一化的超拉普拉斯先验项作为变分能量方程中的光滑项，从而有利于图像在去模糊的求解过程中正确解收敛。由于建立的能量方程不是严格凸的函数，故引入了分裂方法进行求解。整个运动模糊退化图像的盲复原过程在多尺度框架下由粗到细尺度渐进执行。最后利用估计出的点扩展函数计算清晰图像。相对于传统的盲复原算法，本文提出的算法不需要预测图像的梯度信息和对梯度进行筛选，直接求解能量方程就能够得到相应的正确解。得到的结果验证了本文算法的有效性。

Abstract

An blind image restoration method for degraded motion images is proposed based on the variational method. As image gradient is coincident with the probability distribution of heavy-tailed characteristics in a natural scene

the method uses normalized hyper Laplacian prior term as a smooth term of variational energy equation to converge the solution of the equation exactly in image debluring. To reduce the complexity of equation solution

a fast method called split method is introduced. A multi-scale framework is established to perform the deblur procedure from large scale to small scale until the blur kernel is gotten. Finally

the estimated kernel function and clear image are used as the initial value for the next scale. Using the estimated kernel

the final clear image can be gotten by the total variation method. As compared with traditional image restoration methods

the proposed method can obtain the exact solution by direction solution of the energy equation conveniently without predetermining and selecting the image gradients. Experiments demonstrate the feasibility and validity of the proposed method.

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references

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