Combined First- and Second-Order Variational Model for Image Compressive Sensing. (23rd October 2013)

• Record Type: Journal Article
• Authors: Feng, Can; Xiao, Liang; Wei, Zhihui
• Other Names: Yang Suh-Yuh Academic Editor.
• Is Part Of: Mathematical problems in engineering. Volume 2013(2013)
• Publication Date: 2013-10-23
• Subjects: Engineering mathematics -- Periodicals; 510.2462
• DOI: 10.1155/2013/470165
• Languages: English
• ISSNs: 1024-123X

Abstract

Abstract : A hybrid variational model combined first- and second-order total variation for image reconstruction from its finite number of noisy compressive samples is proposed in this paper. Inspired by majorization-minimization scheme, we develop an efficient algorithm to seek the optimal solution of the proposed model by successively minimizing a sequence of quadratic surrogate penalties. Both the nature and magnetic resonance (MR) images are used to compare its numerical performance with four state-of-the-art algorithms. Experimental results demonstrate that the proposed algorithm obtained a significant improvement over related state-of-the-art algorithms in terms of the reconstruction relative error (RE) and peak signal to noise ratio (PSNR).