Blur detection using a neural network

Chong Sze TONG*

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review

3 Citations (Scopus)


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 languageEnglish
Pages (from-to)348-358
Number of pages11
JournalProceedings of SPIE - The International Society for Optical Engineering
Publication statusPublished - 1995
EventAdvanced Signal Processing Algorithms - San Diego, United States
Duration: 9 Jul 1995 → …

Scopus Subject Areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

User-Defined Keywords

  • blur detection
  • defocus
  • image restoration
  • motion blur
  • neural networks


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