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Proceedings Paper

Denoising and interpolation of noisy Bayer data with adaptive cross-color filters
Author(s): Dmitriy Paliy; Alessandro Foi; Radu Bilcu; Vladimir Katkovnik
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Paper Abstract

We propose a novel approach for joint denoising and interpolation of noisy Bayer-patterned data acquired from a digital imaging sensor (e.g., CMOS, CCD). The aim is to obtain a full-resolution RGB noiseless image. The proposed technique is specifically targeted to filter signal-dependant, e.g. Poissonian, or heteroscedastic noise, and effectively exploits the correlation between the different color channels. The joint technique for denoising and interpolation is based on the concept of local polynomial approximation (LPA) and intersection of confidence intervals (ICI). These directional filters utilize simultaneously the green, red, and blue color channels. This is achieved by a linear combination of complementary-supported smoothing and derivative kernels designed for the Bayer data grid. With these filters, the denoised and the interpolated estimates are obtained by convolutions over the Bayer data. The ICI rule is used for data-adaptive selection of the length of the designed cross-color directional filter. Fusing estimates from multiple directions provides the final anisotropic denoised and interpolated values. The full-size RGB image is obtained by placing these values into the corresponding positions in the image grid. The efficiency of the proposed approach is demonstrated by experimental results with simulated and real camera data.

Paper Details

Date Published: 28 January 2008
PDF: 13 pages
Proc. SPIE 6822, Visual Communications and Image Processing 2008, 68221K (28 January 2008); doi: 10.1117/12.766217
Show Author Affiliations
Dmitriy Paliy, Tampere Univ. of Technology (Finland)
Alessandro Foi, Tampere Univ. of Technology (Finland)
Radu Bilcu, Nokia Research Ctr. (Finland)
Vladimir Katkovnik, Tampere Univ. of Technology (Finland)


Published in SPIE Proceedings Vol. 6822:
Visual Communications and Image Processing 2008
William A. Pearlman; John W. Woods; Ligang Lu, Editor(s)

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