Fast algorithm for motion blurred image blind deconvolution

LIAO Yong-zhong1; 2; CAI Zi-xing1; HE Xiang-hua2

doi:10.3788/OPE.20132110.2688

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Fast algorithm for motion blurred image blind deconvolution

更新时间：2020-08-12

- Fast algorithm for motion blurred image blind deconvolution
- Optics and Precision Engineering Vol. 21, Issue 10, Pages: 2688-2695(2013)
- 作者机构：
  
  1. 湖南第一师范学院信息科学与工程系,湖南长沙,410205
  2. 中南大学信息科学与工程学院，湖南长沙 410081
- 作者简介：
- 基金信息：
- DOI：10.3788/OPE.20132110.2688
  CLC： TP391.4
- Received：04 March 2013，
  
  Revised：14 May 2013，
  
  Published Online：19 October 2012，
  
  Published：15 October 2013
- 稿件说明：
移动端阅览
廖永忠蔡自兴何湘华. 运动模糊图像盲解卷积快速算法[J]. 光学精密工程, 2013,21(10): 2688-2695

LIAO Yong-zhong CAI Zi-xing HE Xiang-hua. Fast algorithm for motion blurred image blind deconvolution[J]. Editorial Office of Optics and Precision Engineering, 2013,21(10): 2688-2695
廖永忠蔡自兴何湘华. 运动模糊图像盲解卷积快速算法[J]. 光学精密工程, 2013,21(10): 2688-2695 DOI： 10.3788/OPE.20132110.2688.

LIAO Yong-zhong CAI Zi-xing HE Xiang-hua. Fast algorithm for motion blurred image blind deconvolution[J]. Editorial Office of Optics and Precision Engineering, 2013,21(10): 2688-2695 DOI： 10.3788/OPE.20132110.2688.

摘要

针对快速运动形成的图像模糊，提出了一种运动模糊图像盲解卷积算法。首先，对被噪声污染的频谱图像进行脊波增强；然后，采用一种新的基于Radon变换的鲁棒算法来确定模糊核函数，该算法在小模糊长度和低信噪比的条件下仍能准确地估计模糊核参数；确定模糊核函数后，采用基于hyper-laplacian先验的快速非盲解卷积算法来恢复模糊图像。实验结果证明，与基于机器学习的R.Fergus的算法相比较，本文算法在获得相近效果的前提下，计算时间从近30 min下降到40 s左右。该算法对合成运动模糊图像和实际相机运动的自然模糊图像都具有较好的恢复效果。

Abstract

For a blurred image caused by fast movement

a fast blind deconvolution algorithm for spatially-invariant motion blurred images was proposed. Firstly

the ridge wave of a frequency spectral image with noises was enhanced. Then a robust algorithm based on Ridgelet transform and Radon transform was used to estimate blur kernels in the frequency domain

by which the lengths and directions of motion blur kernels could be accurately estimated

even for small length parameters and blur images in low SNRs .Furthermore

a fast non-blind deconvolution method based on hyper-laplacian prior was used to restore blur images. Experimental results show that the proposed method can restore a 1 megapixel image in less 40 s. As compared with R. Fergus algorithm based on machine learning

the proposed algorithm reduces the computing time from 30 min to 40 s while keeps the comparable quality. Moreover

the algorithm is effective not only for the artificially blurred images

but also for the naturally blurred images (by camera movement) as well.

关键词

Keywords

references

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