Image Inpainting Method Based Sparse Analysis Model Of Synchronous Dictionary Learning

被引：0
|
作者：
Li, Bin [1 ]
Sun, Baohua [1 ]
Li, Dekun [1 ]
Jiang, Tong [1 ]
Li, Gang [1 ]
Li, Hao [1 ]
机构：
[1] China Acad Space Technol Xian, Xian 710100, Shaanxi, Peoples R China
来源：
2021 INTERNATIONAL CONFERENCE ON IMAGE, VIDEO PROCESSING, AND ARTIFICIAL INTELLIGENCE | 2021年 / 12076卷
关键词：
ASimCO; sparse analysis; image inpainting; PSNR; dictionary learning;
D O I：
10.1117/12.2617933
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this paper we propose a new image inpainting method based on sparse analysis model. The method is formulated on the analysis dictionary. The dictionary is updated using a least squares method. Compared with other image inpainting methods including AMLE Inpainting, Harmonic Inpainting, Mumford-Shah Inpainting and Transport Inpainting algorithm, in the image experiments, the peak signal-to-noise ratio (PSNR) is improved. The results show that our proposed algorithm has better performance.
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收藏
页数：6
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