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