MLP neural network super-resolution restoration for the undersampled low-resolution image

被引：0
|
作者：
Su, BH [1 ]
Jin, WQ [1 ]
Niu, LH [1 ]
Liu, GG [1 ]
机构：
[1] Beijing Inst Technol, Dept Opt Engn, Beijing 100081, Peoples R China
来源：
APPLICATIONS AND SCIENCE OF NEURAL NETWORKS, FUZZY SYSTEMS, AND EVOLUTIONARY COMPUTATION V | 2002年 / 4787卷
关键词：
super-resolution; image restoration; image processing; MLP neura I networks; undersampled;
D O I：
10.1117/12.452494
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
It is difficult to achieve restoration of high frequency information by the traditional algorithms using an undersampled and degraded low-resolution image. Nonlinear algorithms provide a better solution to above problem. As a nonlinear and real-time processing method, a MLP neural network super-resolution restoration for the undersampled and degraded low-resolution image is proposed. Experimental results demonstrate that the proposed approach can achieve super-resolution and a good restored image.
引用
收藏
页码：232 / 235
页数：4
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