Demosaicing of Noisy Data: Spatially Adaptive Approach

摘要

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.