Most digital still cameras acquire imagery with a Color Filter array (CFA), sampling only one color value for each pixel and interpolating the other two color values afterwards. The interpolation process is commonly known as demosaicking. In general, a good demosaicking method should preserve the high-frequency information of the imagery as much as possible, since such information is essential for image visual quality. The two observations that are important for Color Filter Array (CFA) demosaicking are: 1) there is a high correlation between the red, green and blue channels and 2) the green channel is less likely to be aliased than the red and blue channels. This project work proposes an influential demosaicking technique that uses interchannel correlation effectively to retrieve the aliased high frequency information in the red and blue channels. The proposed convex set theoretic reconstruction technique defines constraint sets using both the inter-channel correlation and the observed data, and reconstructs the red and blue channels by projecting the initial estimates onto these constraint sets.