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

页码：232 / 235

页数：4

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