Abstract. Spectral filter array (SFA) technology requires development on demosaicing. The authors extend the linear minimum mean square error with neighborhood method to the spectral dimension. They demonstrate that the method is fast and general on Raw SFA images that span the visible and near infra-red part of the electromagnetic range. The method is quantitatively evaluated in simulation first, then the authors evaluate it on real data by the use of non-reference image quality metrics applied on each band. Resulting images show a much better reconstruction of text and high frequencies at the expense of a zipping effect, compared to the benchmark binary-tree method.
Most of digital cameras today use a color filter array (CFA) and a single sensor to acquire color information of the scene. In this article, we ask which arrangement of colors in the mosaic of the CFA provides the best encoding of the scene. As a solution of the inverse problem of demosaicing, we consider a linear minimum mean squared error model. We used redundancy given by the neighborhood on the sampled image to ensure the stability of the solution. For some CFAs, LMMSE with neighborhood provides equivalent reconstruction results and less variability among the image content compared to edge-directed demosaicing on the Bayer. LMMSE allows comparing CFAs of regular pattern with random ones. We show that mosaics with random arrangement of colors and quasi equal proportion of RGB provide best reconstruction performance.
Color demosaicing is the problem of recovering full color/spectral channel from a subsampled image captured by single-chip digital cameras covered by Color Filter Array (CFA). Several algorithms have been proposed in the literature, however most of them are tuned to a particular arrangement of color filters. In this paper, we propose a generic algorithm based on a simple Neural Network (NN) architecture, which is trained on a small image database and which gives competitive results. To prove our statement, we test our network on several state of art CFAs and a 5 channel Spectral Filter Array (SFA). We demonstrate our result both on simulated images coming from standard image databases and also RAW images for a 5 channel SFA camera.
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