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

Binghua Su; Weiqi Jin; Lihong Niu; Guangrong Liu

doi:10.1117/12.452494

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

Binghua Su, Weiqi Jin, Lihong Niu, Guangrong Liu

School of Optics and Photonics

Beijing Institute of Technology

Research output: Contribution to journal › Article › peer-review

2 Citations (Scopus)

Abstract

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.

Original language	English
Pages (from-to)	232-235
Number of pages	4
Journal	Proceedings of SPIE - The International Society for Optical Engineering
Volume	4787
DOIs	https://doi.org/10.1117/12.452494
Publication status	Published - 2002

Keywords

Image processing
Image restoration
MLP neural networks
Super-resolution
Undersampled

Access to Document

10.1117/12.452494

Cite this

@article{2a9608481ce34ab99a5e907d28c1bba3,

title = "MLP neural network super-resolution restoration for the undersampled low-resolution image",

abstract = "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.",

keywords = "Image processing, Image restoration, MLP neural networks, Super-resolution, Undersampled",

author = "Binghua Su and Weiqi Jin and Lihong Niu and Guangrong Liu",

year = "2002",

doi = "10.1117/12.452494",

language = "English",

volume = "4787",

pages = "232--235",

journal = "Proceedings of SPIE - The International Society for Optical Engineering",

issn = "0277-786X",

publisher = "SPIE",

}

TY - JOUR

T1 - MLP neural network super-resolution restoration for the undersampled low-resolution image

AU - Su, Binghua

AU - Jin, Weiqi

AU - Niu, Lihong

AU - Liu, Guangrong

PY - 2002

Y1 - 2002

N2 - 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.

AB - 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.

KW - Image processing

KW - Image restoration

KW - MLP neural networks

KW - Super-resolution

KW - Undersampled

UR - http://www.scopus.com/inward/record.url?scp=0036989573&partnerID=8YFLogxK

U2 - 10.1117/12.452494

DO - 10.1117/12.452494

M3 - Article

AN - SCOPUS:0036989573

SN - 0277-786X

VL - 4787

SP - 232

EP - 235

JO - Proceedings of SPIE - The International Society for Optical Engineering

JF - Proceedings of SPIE - The International Society for Optical Engineering

ER -

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

Abstract

Keywords

Access to Document

Other files and links

Fingerprint

Cite this