tailieunhanh - Báo cáo hóa học: " A Bayesian Super-Resolution Approach to Demosaicing of Blurred Images"

Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: A Bayesian Super-Resolution Approach to Demosaicing of Blurred Images | Hindawi Publishing Corporation EURASIP Journal on Applied Signal Processing Volume 2006 Article ID 25072 Pages 1-12 DOI ASP 2006 25072 A Bayesian Super-Resolution Approach to Demosaicing of Blurred Images Miguel Vega 1 Rafael Molina 2 and Aggelos K. Katsaggelos3 1 Departamento de Lenguajes y Sistemas Informaticos Escuela Tecnica Superior de Ingenieria Infomatica Universidad de Granada 18071 Granada Spain 2 Departamento de Ciencias de la Computation e Inteligencia Artificial Escuela Tecnica Superior de Ingenieria Infomatica Universidad de Granada 18071 Granada Spain 3Department of Electrical Engineering and Computer Science Robert R. McCormick School of Engineering and Applied Science Northwestern University Evanston IL 60208-3118 USA Received 10 December 2004 Revised 6 May 2005 Accepted 18 May 2005 Most of the available digital color cameras use a single image sensor with a color filter array CFA in acquiring an image. In order to produce a visible color image a demosaicing process must be applied which produces undesirable artifacts. An additional problem appears when the observed color image is also blurred. This paper addresses the problem of deconvolving color images observed with a single coupled charged device CCD from the super-resolution point of view. Utilizing the Bayesian paradigm an estimate of the reconstructed image and the model parameters is generated. The proposed method is tested on real images. Copyright 2006 Hindawi Publishing Corporation. All rights reserved. 1. INTRODUCTION Most digital color cameras use a single coupled charge device CCD or a single CMOS sensor with a color filter array CFA to acquire color images. Unfortunately the color filter generates different spectral responses at every CCD cell. The most widely used CFA is the Bayer one 1 . It imposes a spatial pattern of two G cells one R and one B cell as shown in Figure 1. Bayer camera pixels convey incomplete color information which needs to be extended to produce a visible

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