Blur detection using a neural network

Chong Sze Tong

doi:10.1117/12.211411

Blur detection using a neural network

Chong Sze Tong^*

^*Corresponding author for this work

Department of Mathematics

Research output: Contribution to journal › Conference article › peer-review

3 Citations (Scopus)

Abstract

Image restoration is an ill-posed inversion problem wherein an estimate of the ideal original image is to be extracted from a noisy and blurred observation. The ability to restore such a degraded digital image usually requires accurate knowledge of the blur function as well as additional information on the original image. Unfortunately, such a priori knowledge is not always accessible. This paper describes an iterative scheme for the identification of the blurring by making use of the neural network paradigm and the assumption of physical constraints on the blurring process.

Original language	English
Pages (from-to)	348-358
Number of pages	11
Journal	Proceedings of SPIE - The International Society for Optical Engineering
Volume	2563
DOIs	https://doi.org/10.1117/12.211411
Publication status	Published - 7 Jun 1995
Event	1995 International Symposium on Optical Science, Engineering, and Instrumentation - San Diego, United States Duration: 9 Jul 1995 → 14 Jul 1995 https://www.spiedigitallibrary.org/conference-proceedings-of-spie/2563.toc (Conference Proceedings)

User-Defined Keywords

blur detection
defocus
image restoration
motion blur
neural networks

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10.1117/12.211411

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title = "Blur detection using a neural network",

abstract = "Image restoration is an ill-posed inversion problem wherein an estimate of the ideal original image is to be extracted from a noisy and blurred observation. The ability to restore such a degraded digital image usually requires accurate knowledge of the blur function as well as additional information on the original image. Unfortunately, such a priori knowledge is not always accessible. This paper describes an iterative scheme for the identification of the blurring by making use of the neural network paradigm and the assumption of physical constraints on the blurring process.",

keywords = "blur detection, defocus, image restoration, motion blur, neural networks",

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note = "Publisher Copyright: {\textcopyright} 2015 SPIE. All Rights Reserved.; 1995 International Symposium on Optical Science, Engineering, and Instrumentation ; Conference date: 09-07-1995 Through 14-07-1995",

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AU - Tong, Chong Sze

PY - 1995/6/7

Y1 - 1995/6/7

N2 - Image restoration is an ill-posed inversion problem wherein an estimate of the ideal original image is to be extracted from a noisy and blurred observation. The ability to restore such a degraded digital image usually requires accurate knowledge of the blur function as well as additional information on the original image. Unfortunately, such a priori knowledge is not always accessible. This paper describes an iterative scheme for the identification of the blurring by making use of the neural network paradigm and the assumption of physical constraints on the blurring process.

AB - Image restoration is an ill-posed inversion problem wherein an estimate of the ideal original image is to be extracted from a noisy and blurred observation. The ability to restore such a degraded digital image usually requires accurate knowledge of the blur function as well as additional information on the original image. Unfortunately, such a priori knowledge is not always accessible. This paper describes an iterative scheme for the identification of the blurring by making use of the neural network paradigm and the assumption of physical constraints on the blurring process.

KW - blur detection

KW - defocus

KW - image restoration

KW - motion blur

KW - neural networks

UR - https://www.scopus.com/pages/publications/0013408878

U2 - 10.1117/12.211411

DO - 10.1117/12.211411

M3 - Conference article

AN - SCOPUS:0013408878

SN - 0277-786X

VL - 2563

SP - 348

EP - 358

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

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

T2 - 1995 International Symposium on Optical Science, Engineering, and Instrumentation

Y2 - 9 July 1995 through 14 July 1995

ER -

Blur detection using a neural network

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