Adaptive super resolution algorithm for under-sampled images / 南方医科大学学报
Journal of Southern Medical University
; (12): 656-658, 2009.
Article
in Zh
| WPRIM
| ID: wpr-233717
Responsible library:
WPRO
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.
Full text:
1
Index:
WPRIM
Main subject:
Time Factors
/
Algorithms
/
Image Processing, Computer-Assisted
/
Methods
/
Motion
Type of study:
Prognostic_studies
Language:
Zh
Journal:
Journal of Southern Medical University
Year:
2009
Type:
Article