Image decomposing for inpainting using compressed sensing in DCT domain

作     者：Qiang LI Yahong HAN Jianwu DANG 

作者机构：School of Computer Science and Technology Tianjin University Tianjin 300072 China Tianjin Key Laboratory of Cognitive Computing and Application Tianjin University Tianjin 300072 China School of Information Science Japan Advanced Institute of Science and Technology Ishikawa 923-1192 Japan 

出 版 物：《Frontiers of Computer Science》 (中国计算机科学前沿（英文版）)

年 卷 期：2014年第8卷第6期

页      面：905-915页

核心收录：

学科分类：08[工学] 080203[工学-机械设计及理论] 0802[工学-机械工程] 081202[工学-计算机软件与理论] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：Acknowledgements This work is partly supported by National Program on Key Basic Research Project (973 Program  2013CB329301)  the National Natural Science Foundation of China (Grant No. 61202166)  and Doctoral Fund of Ministry of Education of China (20120032120042) 

主　　题：image inpainting compressed sensing image decomposing discrete cosine transform 

摘      要：Inpainting images with occlusion or corruption is a challenging task. Most existing algorithms are pixel based, which construct a statistical model from image features. However, in these algorithms, the frequency component is not sufficiently addressed. In this paper, we propose a novel algorithm that utilizes compressed sensing （CS） in frequency domain to reconstruct corrupted images. In order to reconstruct image, we first decompose the image into two functions with different basic characteristics - structure component and textual component. We seek a sparse representation for the functions and use the DCT coefficients of this representation to generate an over-complete dictionary. Experimental results on real world datasets demonstrate the efficacy of our method in image inpainting. We compare our method with three state-of-the-art inpalnting algorithms and demonstrate its advantages in terms of both quantitative and qualitative aspects.