Space-variant blur deconvolution and denoising in the dual exposure problem

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

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

Abstract

In this paper we propose a space-variant blur estimation and effective denoising/deconvolution method for 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 in the underexposed image is introduced by the gain factor applied to the sensor when the ISO is set to an high value. Due to the space variant degradation, the image pair is divided into overlapping patches for processing. The main idea in the deconvolution algorithm is to incorporate a combination of prior image models into a spatially-varying deblurring/denoising framework which is applied to each patch. The method employs a kernel and parameter estimation method to choose between denoising or deblurring each patch. Experiments on both synthetic and real images are provided to validate the proposed approach.

Original languageEnglish (US)
Pages (from-to)396-409
Number of pages14
JournalInformation Fusion
Volume14
Issue number4
DOIs
StatePublished - 2013

Keywords

  • Blind deconvolution
  • Image denoising
  • Image fusion
  • Low light imaging
  • Motion blur

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Information Systems
  • Hardware and Architecture

Fingerprint Dive into the research topics of 'Space-variant blur deconvolution and denoising in the dual exposure problem'. Together they form a unique fingerprint.

Cite this