Conventional single-chip digital cameras use color filter arrays (CFA) to sample different spectral components. Demosaicing algorithms interpolate these data to complete red, green, and blue values for each image pixel, to produce an RGB image. In this article, we propose a novel demosaicing algorithm for the Bayer CFA. For the algorithm design, we assume that, following the concept proposed in (Zhang and Wu, IEEE Trans Image Process 14 (2005), 2167-2178), the initial interpolation estimates of color channels contain two additive components: the true values of color intensities and the errors that are considered as an additive noise. A specially designed signaladaptive filter is used to remove this so-called demosaicing noise. This filter is based on the local polynomial approximation (LPA) and the paradigm of the intersection of confidence intervals applied to select varying scales of LPA. This technique is nonlinear and spatially-adaptive with respect to the smoothness and irregularities of the image. The presented CFA interpolation (CFAI) technique takes significant advantage from assuming that the original data is noise-free. Nevertheless, in many applications, the observed data is noisy, where the noise is treated as an important intrinsic degradation of the data. We develop an adaptation of the proposed CFAI for noisy data, integrating the denoising and CFAI into a single procedure. It is assumed that the data is given according to the Bayer pattern and corrupted by signal-dependant noise common for charge-coupled device and complementary-symmetry/metal-oxide semiconductor sensors. The efficiency of the proposed approach is demonstrated by experimental results with simulated and real data.

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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 speciÞcally targeted to Þlter 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 conÞdence intervals (ICI). These directional Þlters 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 Þlters, 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 Þlter. Fusing estimates from multiple directions provides the Þnal 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.

As it is known, the impulsive noise appears on the image in the form of randomly distributed pixels of random brightness. Impulses themselves usually differ much from the surrounding pixels in brightness. The main topic of the paper is the introduction of the new impulse detection criteria, and their application to such filters as median, rank-order and cellular neural Boolean. Three impulse detectors are considered. The Rank Impulse Detector uses such property of impulse that its rank in variation series is usually quite different from rank of the median. Exponential Median Detector uses the exponent of the difference between the local median and the value of pixel to detect the impulse. Combination of these two detectors forms the Enhanced Rank Impulse Detector and integrates advantages of both of them. In combination with filter it allows iterative filtering without further image destruction.