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Spatial Gaussian Filtering of Bayer Images with Applications to Color Segmentation

A single sensor color imaging device has a color filter array (CFA) laid on top of its photodiodes, which spatially samples bandpassed spectral responses. Hence, with the popular Bayer pattern, at every pixel site either red, green or blue light is measured. A process known as demosaicing interpolates the vector-valued color image from the scalar-valued sensor output, termed here as the Bayer image. In practice image processing algorithms are then applied onto the full-featured vector image, only. The vectorvalued nature of color images makes tasks like segmentation difficult. Also demosaicing is computationally intensive and fast algorithms, like bilinear interpolation, introduce color artifacts. Therefore a color segmentation algorithm is proposed, that works solely on the scalar-valued Bayer image without the need of demosaicing beforehand. Firstly, it is motivated and then qualitatively and quantitatively verified that filtering the Bayer image with a Gaussian gives a good approximation of a monochrome luminance image of the scene. Secondly, the filtered Bayer image is segmented into regions of small gray-value variances using a graph-based segmentation algorithm. Finally, the mean intensity value of pixels comprising a segmented region is separately computed for each color channel from the originally sensed Bayer image. Such a synthesized color vector is then taken as the label for each segmented region. Results for varying parameters of the segmentation algorithm and alternative methods of color determination with their new and corresponding image processing chains are presented.