Adaptive super resolution algorithm for under-sampled images.
- Author:
Jie PENG
1
;
Qi-fei XU
;
Qing-wen LV
;
Zhi-yuan WANG
;
Yan-qiu FENG
;
Wu-fan CHEN
Author Information
1. School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China.
- Publication Type:Journal Article
- MeSH:
Algorithms;
Image Processing, Computer-Assisted;
methods;
Motion;
Time Factors
- From:
Journal of Southern Medical University
2009;29(4):656-658
- CountryChina
- Language:Chinese
-
Abstract:
A new algorithm of adaptive super-resolution (SR) reconstruction based on the regularization parameter is proposed to reconstruct a high-resolution (HR) image from the low-resolution (LR) image sequence, which takes into full account the inaccurate estimates of motion error, point spread function (PSF) and the additive Gaussian noise in the LR image sequence. We established a novel nonlinear adaptive regularization function and analyzed experimentally its convexity to obtain the adaptive step size. This novel algorithm can effectively improve the spatial resolution of the image and the rate of convergence, which is verified by the experiment on optical images.
