Space-variant kernel deconvolution for dual exposure problem

Miguel Tallón*, Javier Mateos, S. Derin Babacan, Rafael Molina, Aggelos K. Katsaggelos

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review

2 Scopus citations

Abstract

In this paper we propose a space-variant kernel estimation method for effective deconvolution when combining different exposure image pairs. The proposed algorithm can be applied to images blurred by both camera and object motion in an efficient manner. 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 main idea in this work is to incorporate a spatially-varying deblurring/denoising which is applied to image patches. The method exploits kernel estimation and error measures to choose between denoising and deblurring each patch. In addition, the proposed approach estimates all necessary parameters automatically without user supervision.

Original languageEnglish (US)
Pages (from-to)1678-1682
Number of pages5
JournalEuropean Signal Processing Conference
StatePublished - Dec 1 2011
Event19th European Signal Processing Conference, EUSIPCO 2011 - Barcelona, Spain
Duration: Aug 29 2011Sep 2 2011

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering

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