16 September 2005 Adaptive cubic convolution interpolation and sequential filtering for color demosaicing of Bayer pattern image sensors
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Abstract
A still or video camera based on a Bayer-type image sensor is inherently an under-sampled system in terms of color pixel reconstruction. Accurate reconstruction of green channel information and minimization of color artifacts are two primary goals in the color demosaicing methods. Unsuccessful demosaicing methods usually come up with large color artifacts, particularly at image areas with fine details. In the proposed method, we first estimate green values at each chrominance pixel position by utilizing cubic convolution interpolation along the direction of the smallest gradient magnitude. We have defined a diamond shaped interpolation kernel and four different gradient directions to facilitate accurate reconstruction of the green channel. Reconstruction of chrominance channels comprises spectral correlation based averaging of neighboring chrominance pixels and a proposed sequential filtering on the econstructed chrominance channels. Due to the introduction of sequential filtering stage, conventional quantitative image quality measures such as PSNR or PESNR are not high but we found that the visual quality as observed from the human visual system is more natural and comfortably vivid reconstruction can be obtained. Moreover, the proposed demosaicing method comprises additions and subtractions for the most part, which makes its implementation more tractable.
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Wonpil Yu, Wonpil Yu, } "Adaptive cubic convolution interpolation and sequential filtering for color demosaicing of Bayer pattern image sensors", Proc. SPIE 5909, Applications of Digital Image Processing XXVIII, 590915 (16 September 2005); doi: 10.1117/12.615852; https://doi.org/10.1117/12.615852
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