An image super-resolution scheme based on compressive sensing with PCA sparse representation

Authors:
Aixin Zhang;Chao Guan;Haomiao Jiang;Jianhua Li
Affiliations:
School of Information Security Engineering, Shanghai Jiao Tong University, Shanghai, P.R. China;School of Information Security Engineering, Shanghai Jiao Tong University, Shanghai, P.R. China;Department of Electrical Engineering, Stanford University, CA;School of Information Security Engineering, Shanghai Jiao Tong University, Shanghai, P.R. China
Venue:
IWDW'12 Proceedings of the 11th international conference on Digital Forensics and Watermaking
Year:
2012

Citing 7
Cited 0

Learning Low-Level Vision

International Journal of Computer Vision - Special issue on statistical and computational theories of vision: modeling, learning, sampling and computing, Part I
Image super-resolution via sparse representation

IEEE Transactions on Image Processing
Single-Image Super-Resolution Based on Decomposition and Sparse Representation

MEDIACOM '10 Proceedings of the 2010 International Conference on Multimedia Communications
Single Image Super-Resolution via Sparse Representation in Gradient Domain

MINES '11 Proceedings of the 2011 Third International Conference on Multimedia Information Networking and Security
Compressed sensing

IEEE Transactions on Information Theory
Multitask dictionary learning and sparse representation based single-image super-resolution reconstruction

Neurocomputing
Image Deblurring and Super-Resolution by Adaptive Sparse Domain Selection and Adaptive Regularization

IEEE Transactions on Image Processing

Quantified Score

Hi-index	0.00

Visualization

Abstract

Image super-resolution (SR) reconstruction has been an important research fields due to its wide applications. Although many SR methods have been proposed, there are still some problems remain to be solved, and the quality of the reconstructed high-resolution (HR) image needs to be improved. To solve these problems, in this paper we propose an image super-resolution scheme based on compressive sensing theory with PCA sparse representation. We focus on the measurement matrix design of the CS process and the implementation of the sparse representation function for the PCA transformation. The measurement matrix design is based on the relation between the low-resolution (LR) image and the reconstructed high-resolution (HR) image. While the implementation of the PCA sparse representation function is based on the PCA transformation process. According to whether the covariance matrix of the HR image is known or not, two kinds of SR models are given. Finally the experiments comparing the proposed scheme with the traditional interpolation methods and CS scheme with DCT sparse representation are conducted. The experiment results both on the smooth image and the image with complex textures show that the proposed scheme in this paper is effective.