DocumentCode
3484214
Title
A new method of images super-resolution restoration by neural networks
Author
Zhang, Liming ; Pan, Fengzhi
Author_Institution
Dept. of Electron. Eng., Fudan Univ., Shanghai, China
Volume
5
fYear
2002
fDate
18-22 Nov. 2002
Firstpage
2414
Abstract
Super-resolution restoration from the tow-resolution image is an ill-posed problem if there´s no assumption. This paper proposes a new super-resolution scheme based on combining neural network with classical interpolation algorithm. It is shown that our method has better performance than existing interpolation algorithms on theory and also better simulation results than conventional and other neural network methods.
Keywords
image resolution; image restoration; interpolation; inverse problems; learning (artificial intelligence); least mean squares methods; neural nets; smoothing methods; LMS training algorithm; down-sampling; forward mapping; ill-posed problem; image superresolution restoration; interpolation algorithm; linear restoration; low-pass filtering; low-resolution image; neural network; residual errors; Computational modeling; Filtering; Image resolution; Image restoration; Interpolation; Low pass filters; Medical simulation; Multi-layer neural network; Neural networks; Nonlinear filters;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Information Processing, 2002. ICONIP '02. Proceedings of the 9th International Conference on
Print_ISBN
981-04-7524-1
Type
conf
DOI
10.1109/ICONIP.2002.1201927
Filename
1201927
Link To Document