Incoherent dictionary learning and sparse representation for single-image rain removal

Hong-zhong TANG; Xiang WANG; Xiao-gang ZHANG; Xiao LI; Li-zhen MAO

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

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Incoherent dictionary learning and sparse representation for single-image rain removal

Papers | 更新时间：2024-06-05

- Incoherent dictionary learning and sparse representation for single-image rain removal
- Journal on Communications Vol. 38, Issue 7, Pages: 28-35(2017)
- 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：
- 基金信息：
  
  The National Natural Science Foundation of China(61573299);The National Natural Science Foundation of China(61673162);The National Natural Science Foundation of China(61672216);The National Natural Science Foundation of China(61602397);The Natural Science Foundation of Hunan Province(2017JJ3315);The Natural Science Foundation of Hunan Province(2017JJ2251);The Natural Science Foundation of Hunan Province(2016JJ3125)
- DOI：10.11959/j.issn.1000-436x.2017149
  CLC： TP391.4
- Online First：2017-07，
  
  Published：25 July 2017
- 稿件说明：
移动端阅览
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：

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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