Bayesian edge-preserving color image reconstruction from color filter array data

Manu Parmar; Stanley J. Reeves; Thomas S. Denney Jr.

doi:10.1117/12.597627

11 March 2005 Bayesian edge-preserving color image reconstruction from color filter array data

Manu Parmar, Stanley J. Reeves, Thomas S. Denney Jr.

Proceedings Volume 5674, Computational Imaging III; (2005) https://doi.org/10.1117/12.597627
Event: Electronic Imaging 2005, 2005, San Jose, California, United States

Abstract

Digital still cameras typically use a single optical sensor overlaid with RGB color filters to acquire a scene. Only one of the three primary colors is observed at each pixel and the full color image must be reconstructed (demosaicked) from available data. We consider the problem of demosaicking for images sampled in the commonly used Bayer pattern. The full color image is obtained from the sampled data as a MAP estimate. To exploit the greater sampling rate in the green channel in defining the presence of edges in the blue and red channels, a Gaussian MRF model that considers the presence of edges in all three color channels is used to define a prior. Pixel values and edge estimates are computed iteratively using an algorithm based on Besag's iterated conditional modes (ICM) algorithm. The reconstruction algorithm iterates alternately to perform edge detection and spatial smoothing. The proposed algorithm is applied to a variety of test images and its performance is quantified by using the CIELAB delta E measure.

Citation Download Citation

Manu Parmar, Stanley J. Reeves, and Thomas S. Denney Jr. "Bayesian edge-preserving color image reconstruction from color filter array data", Proc. SPIE 5674, Computational Imaging III, (11 March 2005); https://doi.org/10.1117/12.597627

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