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.
Conventional approach in single-chip digital cameras is a use of color filter arrays (CFA) in order to sample different spectral components. Demosaicing algorithms interpolate these data to complete red, green, and blue values for each image pixel, in order to produce an RGB image. In this paper we propose a novel demosaicing algorithm for the Bayer CFA. It is assumed that the initial estimates of color channels contain two additive components: the true values of color intensities and the errors. The errors are considered as an additive noise, and often called as a demosaicing noise, that has been removed. However, this noise is not white and strongly depends on a signal. Usually, the intensity of this noise is higher near edges of image details. We use spatially designed signal-adaptive filter to remove the noise. This filter is based on the local polynomial approximation (LPA) and the paradigm of the intersection of confidence intervals (ICI) applied for selection adaptively varying scales (window sizes) of LPA. The LPA-ICI technique is nonlinear and spatially-adaptive with respect to the smoothness and irregularities of the image. The efficiency of the proposed approach is demonstrated by simulation results.
Due to camera module miniaturization, the pixel area of the digital sensors decreases which decreases also the
signal to noise ratio in the captured images. As a consequence, image de-noising is still an important topic
in digital image processing field. In this paper we address the problem of image de-noising using the nonlocal
means algorithm. This method has excellent de-noising properties but at the expense of increasing the
computational complexity. We propose here a novel approach that provides similar filtering capabilities with
much less computational effort and shorter processing time. Our proposed algorithm is compared with the nonlocal
means algorithm and with another fast implementation, recently reported, in terms of processing time and
noise reduction capability (from both visual impression and mean squared error points of view). The comparative
results are presented for artificially degraded images and also for images obtained with a camera phone.