Color image reconstruction from noisy color Þlter array (CFA) data is considered. A modiÞcation of the Block Matching 3D (BM3D) [2] Þlter for CFA data denoising utilizing cross-color correlations is proposed. Denoised images are then demosaicked by algorithms developed for noise-free data leading to state-of-the-art performance for both Gaussian and Poissonian noise models.

In this paper we present a cost-effective solution for combined de-noising and sharpening of digital images. Our method combines the unsharp masking and sigma filtering techniques through a regularization mechanism thus ensuring effective noise reduction and edge enhancement in the processed image. We describe our method in detail and we analyze the proposed implementation through extensive experiments done in various scenarios. Due to its low computational complexity the proposed method is well suited for mobile implementations.

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This paper presents a novel multi-channel image restoration algorithm. The main idea is to develop practical approaches to reduce optical blur from noisy observations produced by the sensor of a camera phone. An iterative deconvolution is applied separately to each color channel directly on the raw data. We use a modified iterative Landweber algorithm combined with an adaptive denoising technique. The adaptive denoising is based on local polynomial approximation (LPA) operating on data windows selected by the rule of intersection of confidence intervals (ICI). In order to avoid false coloring due to independent component filtering in the RGB space, we have integrated a novel saturation control mechanism that smoothly attenuates the high-pass filtering near saturated regions. It is shown by simulations that the proposed filtering is robust with respect to errors in point-spread function and approximated noise models. Experimental results show that the proposed processing technique produces significant improvement in perceived image resolution.