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Demosaicking is standardly the first step in today's Image Signal Processing(ISP) pipeline of digital cameras. It reconstructs image RGB values from thespatially and spectrally sparse Color Filter Array (CFA) samples, which are theoriginal raw data digitized from electrical signals. High quality and low costdemosaicking is not only necessary for photography, but also fundamental formany machine vision tasks. This paper proposes an accurate and fastdemosaicking model based on Convolutional Neural Networks (CNN) for the BayerCFA, which is the most popular color filter arrangement adopted by digitalcamera manufacturers. Observing that each channel has different estimationcomplexity, we reconstruct each channel by an individual sub-network. Moreover,instead of directly estimating the red and blue values, our model infers thegreen-red and green-blue color difference. This strategy allows recovering themost complex channel by a light weight network. Although the total size of ourmodel is significantly smaller than the state of the art demosaicking networks,it achieves substantially higher performance in both demosaicking quality andcomputational cost, as validated by extensive experiments. Source code will bereleased along with paper publication.

Channel-by-Channel Demosaicking Networks with Embedded Spectral Correlation