Super-resolution using regularized orthogonal matching pursuit based on compressed sensing theory in the wavelet domain

Authors:
Tingting Li
Affiliations:
College of Mathematics and Physics, Chongqing University, Chongqing, China
Venue:
CIRA'09 Proceedings of the 8th IEEE international conference on Computational intelligence in robotics and automation
Year:
2009

Citing 8
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
Digital Image Restoration

Digital Image Restoration
Example-Based Super-Resolution

IEEE Computer Graphics and Applications
Limits on Super-Resolution and How to Break Them

IEEE Transactions on Pattern Analysis and Machine Intelligence
Image upsampling via imposed edge statistics

ACM SIGGRAPH 2007 papers
Uniform Uncertainty Principle and Signal Recovery via Regularized Orthogonal Matching Pursuit

Foundations of Computational Mathematics
ForWaRD: Fourier-wavelet regularized deconvolution for ill-conditioned systems

IEEE Transactions on Signal Processing
Signal Recovery From Random Measurements Via Orthogonal Matching Pursuit

IEEE Transactions on Information Theory

Quantified Score

Hi-index	0.00

Visualization

Abstract

We proposed a compressed sensing Super Resolution algorithm based on wavelet. The proposed algorithm performs well with a smaller quantity of training image patches and outputs images with satisfactory subjective quality. It is tested on classical images commonly adopted by Super Resolution researchers with both generic and specialized training sets for comparison with other popular commercial software and state-of-the-art methods. Experiments demonstrate that, the proposed algorithm is competitive among contemporary Super Resolution methods.