QQ客服 官方微博 反馈留言

	IMAGE COMPRESSIVE-SENSING RECOVERY USING STRUCTURED LAPLACIAN SPARSITY IN DCT DOMAIN AND MULTI-HYPOTHESIS PREDICTION
	Zhao, Chen ; Ma, Siwei ; Gao, Wen
	2014
关键词	Compressive sensing image recovery structured Laplacian sparsity multi-hypothesis
英文摘要	In compressive sensing (CS), the seeking of a fair domain is of essentially significance to achieve a high enough degree of signal sparsity. Most methods in the literature, however, use a fixed transform domain or prior information that cannot exhibit enough sparsity for various images. Superiorly, we propose an algorithm to explore the structured Laplacian sparsity of DCT coefficients, which can adapt to the nonstationarity of natural images. Better sparsity is achieved by utilizing the nonlocal similarity of natural images and constructing structured image patch groups. Meanwhile, multiple hypotheses for each pixel could be obtained owing to the overlapping of the structured groups and similar patches. Additionally, for solving the optimization problem formulated from the techniques above, we design an efficient iterative method based on split Bregman iteration (SBI) algorithm. Experimental results demonstrate that the proposed algorithm outperforms the other state-of-the-art methods in both objective and subjective recovery quality.; EI; CPCI-S(ISTP); zhaochen@pku.edu.cn; swma@pku.edu.cn; wgao@pku.edu.cn; Septmber; 2014-September
语种	英语
出处	2014 IEEE INTERNATIONAL CONFERENCE ON MULTIMEDIA AND EXPO (ICME)
DOI标识	10.1109/ICME.2014.6890254
内容类型	其他
源URL	[http://ir.pku.edu.cn/handle/20.500.11897/423837]
专题	信息科学技术学院
推荐引用方式 GB/T 7714	Zhao, Chen,Ma, Siwei,Gao, Wen. IMAGE COMPRESSIVE-SENSING RECOVERY USING STRUCTURED LAPLACIAN SPARSITY IN DCT DOMAIN AND MULTI-HYPOTHESIS PREDICTION. 2014-01-01.

个性服务

查看访问统计

相关权益政策

暂无数据

收藏/分享

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。