Digital Still Color Cameras sample the visible spectrum using an array of color filters overlaid on a CCD such that each pixel samples only one color band. The resulting mosaic of color samples is processed to produce a high resolution color image such that a value of a color band not sampled at a certain location is estimated from its neighbors. This is often referred to as 'demosaicking.' In this paper, we approach the process of demosaicking as a bilateral filtering process which is a combination of spatial domain filtering and filtering based on similarity measures. Bilateral filtering smooths images while preserving edges by means of nonlinear combinations of neighboring image pixel values. A bilateral filter can enforce similarity metrics (such as squared error or error in the CIELAB space) between neighbors while performing the typical filtering operations. We have implemented a variety of kernel combinations while performing demosaicking. This approach provides us with a means to denoise, sharpen and demosaic the image simultaneously. We thus have the ability to represent demosaicking algorithms as spatial convolutions. The proposed method along with a variety of existing demosaicking strategies are run on synthetic images and real-world images for comparative purposes.