面向单幅图像去雨的非相干字典学习及其稀疏表示研究

汤红忠; 王翔; 张小刚; 李骁; 毛丽珍

doi:10.11959/j.issn.1000-436x.2017149

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面向单幅图像去雨的非相干字典学习及其稀疏表示研究

学术论文 | 更新时间：2024-06-05

- 面向单幅图像去雨的非相干字典学习及其稀疏表示研究
- Incoherent dictionary learning and sparse representation for single-image rain removal
- 通信学报 2017年38卷第7期页码：28-35
- 作者机构：
  
  1. 湘潭大学信息工程学院，湖南湘潭 411105
  2. 湖南大学电气与信息工程学院，湖南长沙 410082
  3. 湘潭大学控制工程研究所，湖南湘潭 411105
- 作者简介：
  
  [ "汤红忠（1979-），女，湖南衡山人，湖南大学博士生，湘潭大学副教授，主要研究方向为图像处理与模式识别、字典学习及稀疏表示。" ]
  [ "王翔（1991-），男，湖南衡阳人，湘潭大学硕士生，主要研究方向为图像处理与模式识别。" ]
  [ "张小刚（1972-），男，河南汝南人，湖南大学教授、博士生导师，主要研究方向为工业过程控制与模式识别。" ]
  [ "李骁（1993-），男，湖南临湘人，湘潭大学硕士生，主要研究方向为图像处理与模式识别。" ]
  [ "毛丽珍（1994-），女，湖南岳阳人，湘潭大学硕士生，主要研究方向为图像处理与模式识别。" ]
- 基金信息：
  
  国家自然科学基金资助项目(61573299);国家自然科学基金资助项目(61673162);国家自然科学基金资助项目(61672216);国家自然科学基金资助项目(61602397);湖南省自然科学基金资助项目(2017JJ3315);湖南省自然科学基金资助项目(2017JJ2251);湖南省自然科学基金资助项目(2016JJ3125)
- DOI：10.11959/j.issn.1000-436x.2017149
  中图分类号： TP391.4
- 网络首发：2017-07，
  
  纸质出版：2017-07-25
- 稿件说明：
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汤红忠, 王翔, 张小刚, 等. 面向单幅图像去雨的非相干字典学习及其稀疏表示研究[J]. 通信学报, 2017,38(7):28-35.

Hong-zhong TANG, Xiang WANG, Xiao-gang ZHANG, et al. Incoherent dictionary learning and sparse representation for single-image rain removal[J]. Journal on Communications, 2017, 38(7): 28-35.
汤红忠, 王翔, 张小刚, 等. 面向单幅图像去雨的非相干字典学习及其稀疏表示研究[J]. 通信学报, 2017,38(7):28-35. DOI： 10.11959/j.issn.1000-436x.2017149.

Hong-zhong TANG, Xiang WANG, Xiao-gang ZHANG, et al. Incoherent dictionary learning and sparse representation for single-image rain removal[J]. Journal on Communications, 2017, 38(7): 28-35. DOI： 10.11959/j.issn.1000-436x.2017149.

摘要

提出一种非相干字典学习及稀疏表示方法，并将其应用于单幅图像去雨。该方法在字典学习阶段，为降低有雨原子与无雨原子间的相似性，引入字典的非相干性，构建新的目标函数，不仅可以保证有雨字典与无雨字典的可分性，而且学习的非相干字典具有类似于紧框架的性质，可以逼近等角紧框架。通过有雨字典与无雨字典对高频图像的稀疏表示，能够更好地分离出高频图像中的有雨分量与无雨分量，将高频无雨分量与低频图像融合实现图像去雨。采用合成雨图与真实雨图对算法进行验证，实验结果表明，算法所学习的非相干字典具有较好的稀疏表示性能，去雨后的图像雨线残留较少，边缘细节保持较好，视觉效果更为清晰自然。

Abstract

The incoherent dictionary learning and sparse representation algorithm was present and it was applied to single-image rain removal.The incoherence of the dictionary was introduced to design a new objective function in the dictionary learning

which addressed the problem of reducing the similarity between rain atoms and non-rain atoms.The divisibility of rain dictionary and non-rain dictionary could be ensured.Furthermore

the learned dictionary had similar properties to the tight frame and approximates the equiangular tight frame.The high frequency in the rain image could be decomposed into a rain component and a non-rain component by performing sparse coding based learned incoherent dictionary

then the non-rain component in the high frequency and the low frequency were fused to remove rain.Experimental results demonstrate that the learned incoherent dictionary has better performance of sparse representation.The recovered rain-free image has less residual rain

and preserves effectively the edges and details.So the visual effect of recovered image is more sharpness and natural.

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references

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