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.
In this paper we propose a novel color demosaicing algorithm for noisy data. It is assumed that the data is given according to the Bayer pattern and corrupted by signal-dependant noise which is common for CCD and CMOS digital image sensors. Demosaicing algorithms are used to reconstruct missed red, green, and blue values to produce an RGB image. This is an interpolation problem usually called color filter array interpolation (CFAI). The conventional approach used in image restoration chains for the noisy raw sensor data exploits denoising and CFAI as two independent steps. The denoising step comes first and the CFAI is usually designed to perform on noiseless data. In this paper we propose to integrate the denoising and CFAI into one procedure. Firstly, we compute initial directional interpolated estimates of noisy color intensities. Afterward, these estimates are decorrelated and denoised by the special directional anisotropic adaptive filters. This approach is found to be efficient in order to attenuate both noise and interpolation errors. The exploited denoising technique is based on the local polynomial approximation (LPA). The adaptivity to data is provided by the multiple hypothesis testing called the intersection of confidence intervals (ICI) rule which is applied for adaptive selection of varying scales (window sizes) of LPA. We show the efficiency of the proposed approach in terms of both numerical and visual evaluation.
In this paper, we propose a novel nonparametric approach to reconstruction of three-dimensional (3D) objects from 2D blurred and noisy observations which is a problem of computational optical sectioning. This approach is based on an approximate image formation model which takes into account depth varying nature of blur described by a matrix of shift-invariant 2D point-spread functions (PSF) of an optical system. The proposed restoration scheme incorporates the matrix regularized inverse and matrix regularized Wiener inverse algorithms in combination with a novel spatially adaptive denoising. This technique is based on special statistical rules for selection of the adaptive size and shape neighbourhood used for the local polynomial approximation of the 2D image intensity. The simulations on a phantom 3D object show efficiency of the developed approach. The objective result evaluation is presented in terms of quadratic-error criteria.
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.
A new approach to impulsive noise filtering is considered in the paper. It is known that the median filter is a sliding window filter, with a window N X N. As it is known, the impulsive noise is a 'big' and unusual jump of brightness. So if the central pixel in the window is noisy, its value belongs to one of the ends of the variation representation of the local histogram. We analyze, where the value of central pixel of the window is positioned in the variation series. If it is positioned close to the boarders, we can assume that it is an impulsive corruption and it must be filtered. This kind of analysis could be used for the improvement of many filters: median, rank-order, cellular neural, etc. So, implementing such kind of preliminary noise detection, we achieve the good results. Filters became gentler and less destructive for the image, but steel very effective.
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