A convolutional neural network is trained in auto/hetero-associative mode for reconstructing RGB components from a randomly mosaicked color image. The was shown to perform equally well when images are sampled periodically or with different random mosaic. Therefore, this model able generalize on every type of filter array. We attribute property universal demosaicking the learning statistical str...

Linear interpolation methods have the characteristics of low computational complexity which makes them widely developed in CFA (color filter array) demosaicking. However, the trichromatic nature of colour images enables CFA demosaicking algorithms to take advantage of the luminance-chrominance representation to interpolate the colour planes efficiently and effectively. It seems, however, this d...

This paper addresses the problem of interpolating missing color components at the output of a Bayer color filter array (CFA), a process known as demosaicking. A luma-chroma demultiplexing algorithm is presented in detail, using a least-squares design methodology for the required bandpass filters. A systematic study of objective demosaicking performance and system complexity is carried out, and ...

We propose a novel method for image demosaicking from samples obtained with a completely arbitrary color filter array (CFA). We adopt a variational approach where the reconstructed image has maximal smoothness under the constraint of consistency with the available measurements. This optimization problem boils down to a large, sparse system of linear equations to solve, for which we propose an i...

Digital cameras to reduce cost, use an image sensor to capture color images. Color Filter Array (CFA) in digital cameras permits only one of the three primary (red-green-blue) colors to be sensed in a pixel and interpolates the two missing components through a method named demosaicking. Captured data is interpolated into a full color image and compressed in applications. Color interpolation bef...

Multispectral imaging systems are currently expanding with a variety of multispectral demosaicking algorithms. But these algorithms have limitations due to the remarkable presence artifacts in reconstructed image. In this paper, we propose powerful image method that focuses on G band and luminance component. We've first identified relevant 4-and 5-band filter array (MSFA) dominant then proposed...

Sparse coding---that is, modeling data vectors as sparse linear combinations of dictionary elements---is widely used in machine learning, neuroscience, signal processing, and statistics. This talk addresses the problem of learning the dictionary, adapting it to specific data and image understanding tasks. In particular, I will present a fast on-line approach to unsupervised dictionary learning ...

The supplementary material contains the proof of Remark 3.1 as well as some additional information about the numerical experiments that contribute to the understanding of the main paper. We present detailed qualitative and quantitative evaluation results for each of our two (demosaicking and deconvolution) exemplary linear inverse image reconstruction problems. These results include parameter v...