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.

引用

页数：6

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