An Improved morphological component analysis algorithm for Tangka image inpainting

doi:10.1109/CISP.2013.6744016

	An Improved morphological component analysis algorithm for Tangka image inpainting
	Hu, Wen-Jin; Li, Zhan-Ming; Liu, Zhong-Min
	2013
会议日期	December 16, 2013 - December 18, 2013
会议地点	Hangzhou, China
关键词	Computational complexity Image analysis Image denoising Image reconstruction Iterative methods Learning algorithms Stairs Textures Complexity of algorithm Conventional modeling Dictionary learning algorithms Fast algorithms Image Inpainting Morphological component analysis Non-local means Sparse representation
卷号	1
DOI	10.1109/CISP.2013.6744016
页码	346-351
英文摘要	This paper proposed a new image inpainting method based on morphological component analysis that is capable of filling in holes in overlapping texture and cartoon layers. Firstly, due to rich content and complex color of Tangka image, the imposition of a total variation penalty by conventional model may not be accurate and easy to produce staircase. To improve the performance of sparse-representation-based image decomposition, in this paper the concept of non-local means which explicitly exploits self-similarities is introduced. In addition to, using fast algorithm reduce effectively the calculation of not related pixel weights within area, so the complexity of algorithm is reduced. The novel model preserve the fine structure, details and texture and eliminate staircase simultaneously, which make the subsequent iteration is more effective. Secondly, in order to improve the performance of sparse representation based image restoration, the concept of an example patches-aided dictionary learning algorithm named KSVD algorithm is adopted. Experimental results for thangka image which contains scratch and block loss show that the proposed method achieves better inpainting effect. © 2013 IEEE.
会议录	Proceedings of the 2013 6th International Congress on Image and Signal Processing, CISP 2013
会议录出版者	IEEE Computer Society
语种	英语
内容类型	会议论文
源URL	[http://ir.lut.edu.cn/handle/2XXMBERH/117515]
专题	电气工程与信息工程学院
作者单位	College of Electrical and Information Engineering, School of Math and Computer Science, Lanzhou University of Technology, Lanzhou, China
推荐引用方式 GB/T 7714	Hu, Wen-Jin,Li, Zhan-Ming,Liu, Zhong-Min. An Improved morphological component analysis algorithm for Tangka image inpainting[C]. 见:. Hangzhou, China. December 16, 2013 - December 18, 2013.