Abstract
Demosaicking is the way toward reproducing a full hued picture from a deficient shaded picture. The single sensor doesn't catch all hues for a single pixel. To address this, a color filter array (CFA) is utilized to get a hued picture from a single sensor. The created picture from CFA is called a mosaic picture. In this research, we utilize specialized networks to remove the noise from Bayer images. The mosaic picture is adulterated by commotion presented by a sensor or other equipment during catching. Demosaicking on the boisterous mosaic picture makes antiquities, for example, moire and zippering. Some solutions have been proposed for denoising mosaic images but they are handcrafted solutions. In this paper, a solution is proposed to the first denoise and then demosaic the image using machine learning. The mosaic image is denoised using CNN which is then demosaicked using the residual learning strategy of a single specialized network. One of the networks is DHTN (deep high textured network) which is trained on textured images and the second one is DSTN (deep smooth textured network) which is trained on smooth images. Preliminary results show that the proposed approach generates better results and higher quality images than traditional approaches.