Full Length Article: Space-variant blur deconvolution and denoising in the dual exposure problem

Authors:
Miguel TallóN;Javier Mateos;S. Derin Babacan;Rafael Molina;Aggelos K. Katsaggelos
Affiliations:
Dept. de Ciencias de la Computación e I.A., Universidad de Granada, Granada, Spain;Dept. de Ciencias de la Computación e I.A., Universidad de Granada, Granada, Spain;Beckman Institute, University of Illinois at Urbana-Champaign, IL, USA;Dept. de Ciencias de la Computación e I.A., Universidad de Granada, Granada, Spain;Dept. of Electrical Engineering & Computer Science, Northwestern University, Evanston, IL, USA
Venue:
Information Fusion
Year:
2013

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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.