DocumentCode :
677280
Title :
Image decomposition model combined with sparse representation and total variation
Author :
Xuan Zhu ; Ning Wang ; Enbiao Lin ; Qiuju Li ; Xufeng Zhang
Author_Institution :
Sch. of Inf. Sci. & Technol., Northwest Univ., Xi´an, China
fYear :
2013
fDate :
26-28 Aug. 2013
Firstpage :
86
Lastpage :
91
Abstract :
In this paper, we propose a new decomposition model combined with sparse representation and total variation (SRTV), which allows us to separate cartoon and texture components from an image. The SRTV model naturally fits into the framework of separation and produces separated layers, meanwhile, denoising and inpainting process appears as the byproducts. Therefore, the new approach incorporates separation, denoising, and inpainting as a unified framework. We demonstrate the performance of the new approach through several examples.
Keywords :
image denoising; image representation; image segmentation; image texture; SRTV; cartoon; denoising process; image decomposition model; inpainting process; separation; sparse representation; texture component; total variation; Analytical models; Dictionaries; Image decomposition; Mathematical model; Noise reduction; Optimized production technology; Transforms; Total variation; decomposition; denosing; inpainting; sparse representation;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Information and Automation (ICIA), 2013 IEEE International Conference on
Conference_Location :
Yinchuan
Type :
conf
DOI :
10.1109/ICInfA.2013.6720275
Filename :
6720275
Link To Document :
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