Spatially adaptive color filter array interpolation for noiseless and noisy data

Abstract

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.

Cross-color BM3D filtering of noisy raw data

“…In the presence of noise, the performance of the such algorithms degrades drastically. Three main strategies to deal with noisy data are possible: denoising after demosaicking, joint demosaicking-denoising (e.g., [5], [9], [10], [11]), and denoising before demosaicking (e.g., [7]). Denoising after demosaicking is very challenging, because sophisticated adaptive interpolation procedures change the statistical model of the noise in a complex and hardly computable form.…”

“…Design of efÞcient joint algorithms is not an easy task because of the antagonistic nature of the denoising and interpolation procedures: denoising mainly performing some sort of data smoothing, while interpolation aims at reconstructing missing high-frequency details. The third approach, denoise and then demosaick, while apparently simple and straightforward, was long time considered to be inefÞcient [10], [9]. Direct application of conventional grayscale denoising Þlters to CFA is problematic due to the underlying mosaic structure of the CFA, which violates the basic assumptions about local smoothness in natural images which these Þlters rely upon.…”

Joint Demosaicing and Denoising via Learned Nonparametric Random Fields

“…To high accuracy, the true signal λ plus shot Method and reference Abbreviation Bilinear Interpolation [36] BI Matlab demosaic(.) function MBI Directional MMSE [19] D-MMSE Weighted Edge and Colors Difference [30] WECD One Step AP [21] OS-AP Alternating Projections [10] AP Posterior Directional Filtering [18] PDF Non-Local Adaptive Thresholding [31] NAT Non-Local Means [31] NLM Adaptive Homogeneity-Directed Demosaicing [17] AHD Adaptive color plan interpolation (Hamilton & Adams) [12] HA Local Polynomial Approximation [37] LPA Contour Stencils [33] CS Regression Tree Field (this work) RTF [38]. Noise affects demosaicing algorithms in a number of ways.…”

“…Finally, the blue and red channels are recovered. Similarly, Paliy et al [37] suggest a spatially adaptive nonlinear filter by using local polynomial approximations, to eliminate the demosaicing noise generated in the demosaicing process. The model by Menon and Calvagno [45] utilizes space-varying filters, the parameters of which are optimized with a quadratic regularization term.…”

Spatial-Temporal Color Video Reconstruction From Noisy CFA Sequence

“…It first performs demosaickingdenoising on the green channel, and then uses the restored green channel to estimate the noise statistics to restore the red and blue channels. Inspired by the directional linear minimum mean square-error estimation based CDM scheme in [4], Paliy et al [21], [22] proposed a nonlinear and spatially adaptive filter by using local polynomial approximation for CDM and then adapted this scheme to noisy CFA inputs for joint demosaicking-denoising.…”