通信学报2017，Vol.38Issue(7)：28-35,8.DOI:10.11959/j.issn.1000-436x.2017149
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
摘要
Abstract
The incoherent dictionary learning and sparse representation algorithm was present and it was applied to sin-gle-image rain removal. The incoherence of the dictionary was introduced to design a new objective function in the dic-tionary learning, which addressed the problem of reducing the similarity between rain atoms and non-rain atoms. The di-visibility 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 dic-tionary, then the non-rain component in the high frequency and the low frequency were fused to remove rain. Experi-mental results demonstrate that the learned incoherent dictionary has better performance of sparse representation. The re-covered rain-free image has less residual rain, and preserves effectively the edges and details. So the visual effect of re-covered image is more sharpness and natural.关键词
非相干字典/字典学习/稀疏表示/单幅图像去雨Key words
incoherent dictionary/dictionary learning/sparse representation/single-image rain removal分类
信息技术与安全科学引用本文复制引用
汤红忠,王翔,张小刚,李骁,毛丽珍..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):28-35,8.基金项目
国家自然科学基金资助项目(No.61573299, No.61673162, No.61672216, No.61602397) （No.61573299, No.61673162, No.61672216, No.61602397）
湖南省自然科学基金资助项目(No.2017JJ3315, No.2017JJ2251, No.2016JJ3125) The National Natural Science Foundation of China (No.61573299, No.61673162, No.61672216, No.61602397), The Natural Science Foundation of Hunan Province (No.2017JJ3315, No.2017JJ2251, No.2016JJ3125) （No.2017JJ3315, No.2017JJ2251, No.2016JJ3125）
