Image prior combination in space-variant blur deconvolution for the dual exposure problem

Miguel Tallón*, Javier Mateos, Rafael Molina, Aggelos K Katsaggelos

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

In this paper we propose a space-variant blur estimation and effective deconvolution method when combining a long exposure blurry image with a short exposure noisy one. The blur in the long exposure shot is mainly caused by camera shake or object motion, and the noise of the underexposed image is introduced by the gain factor applied to the sensor when the ISO is set to a high value. The image pair is divided in overlapping patches for processing. The main idea in this work is to incorporate a combination of prior image models to a spatially-varying deblurring/denoising framework which is applied to each patch. The method exploits kernel and parameters estimation to choose between denoise or deblur each patch. In addition, the proposed approach estimates all necessary parameters automatically without user supervision. Experiments on both synthetic and real images validate the used approach.

Original languageEnglish (US)
Title of host publicationISPA 2011 - 7th International Symposium on Image and Signal Processing and Analysis
Pages408-413
Number of pages6
StatePublished - Dec 20 2011
Event7th International Symposium on Image and Signal Processing and Analysis, ISPA 2011 - Dubrovnik, Croatia
Duration: Sep 4 2011Sep 6 2011

Other

Other7th International Symposium on Image and Signal Processing and Analysis, ISPA 2011
CountryCroatia
CityDubrovnik
Period9/4/119/6/11

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Signal Processing

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