Image Denoising Based on Online Dictionary Learning

被引:0
作者
Ni Hao [1 ]
Hu Yonghong [2 ]
Liu Fanghu [1 ]
Wu Aixi [3 ]
Ruan Ruolin [4 ]
Mao Caixia [1 ]
机构
[1] Hubei Univ Sci & Technol, Sch Elect & Informat Engn, Xianning, Peoples R China
[2] Hubei Univ Sci & Technol, Sch Nucl Technol & Chem Biol, Xianning, Peoples R China
[3] Hubei Univ Sci & Technol, Student Affairs Dept, Xianning, Peoples R China
[4] Hubei Univ Sci & Technol, Sch Biomed Engn, Xianning, Peoples R China
来源
2018 10TH INTERNATIONAL CONFERENCE ON COMMUNICATION SOFTWARE AND NETWORKS (ICCSN) | 2018年
基金
湖北省教育厅重点项目;
关键词
image denoising; online dictioanry learning; sparse coding; image restoration; SPARSE REPRESENTATION;
D O I
暂无
中图分类号
TP31 [计算机软件];
学科分类号
081202 ; 0835 ;
摘要
Many state-of-the- art denoising algorithms often employ dictioanary learning methods to acquire the mapping relationship between the polluted image by noise and the original clean image. It is critical to generate the appropriate dictionary in image denoising based on dictionary learning. In order to promote the denoising efficiency, the proposed algorithm employs Online Dictionary Learning (ODL) to train the overcomplete dictionaries to make the dictionary more accurate. The dictionary updating procedure is improved with a warm start. The dictionary is updated by the last computed dictionary and the current input image patches. Hence the dictionary is more accurate to get better denoising images. In the experiments, the PSNR of ODL dictionary is 0.12dB higher than SCDL and 0.21dB higher than K-SVD in average. The proposed method can eliminate the artifacts and recover the texture details efficiently.
引用
收藏
页码:547 / 551
页数:5
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