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http://dx.doi.org/10.5573/ieie.2014.51.6.209

Compressive Sensing Recovery of Natural Images Using Smooth Residual Error Regularization  

Trinh, Chien Van (College of Information and Communication Engineering, Sungkyunkwan University)
Dinh, Khanh Quoc (College of Information and Communication Engineering, Sungkyunkwan University)
Nguyen, Viet Anh (College of Information and Communication Engineering, Sungkyunkwan University)
Park, Younghyeon (College of Information and Communication Engineering, Sungkyunkwan University)
Jeon, Byeungwoo (College of Information and Communication Engineering, Sungkyunkwan University)
Publication Information
Journal of the Institute of Electronics and Information Engineers / v.51, no.6, 2014 , pp. 209-220 More about this Journal
Abstract
Compressive Sensing (CS) is a new signal acquisition paradigm which enables sampling under Nyquist rate for a special kind of signal called sparse signal. There are plenty of CS recovery methods but their performance are still challenging, especially at a low sub-rate. For CS recovery of natural images, regularizations exploiting some prior information can be used in order to enhance CS performance. In this context, this paper addresses improving quality of reconstructed natural images based on Dantzig selector and smooth filters (i.e., Gaussian filter and nonlocal means filter) to generate a new regularization called smooth residual error regularization. Moreover, total variation has been proved for its success in preserving edge objects and boundary of reconstructed images. Therefore, effectiveness of the proposed regularization is verified by experimenting it using augmented Lagrangian total variation minimization. This framework is considered as a new CS recovery seeking smoothness in residual images. Experimental results demonstrate significant improvement of the proposed framework over some other CS recoveries both in subjective and objective qualities. In the best case, our algorithm gains up to 9.14 dB compared with the CS recovery using Bayesian framework.
Keywords
Compressive sensing; Total variation; Augmented Lagrangian method; Dantzig selector; Smooth filter;
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Times Cited By KSCI : 2  (Citation Analysis)
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