Demosaicing of Color Images by Accurate Estimation of Luminance

Digital cameras acquire color images using a single sensor with Color filter Arrays. A single color component per pixel is acquired using color filter arrays and the remaining two components are obtained using demosaicing techniques. The conventional demosaicing techniques existent induce artifacts in resultant images effecting reconstruction quality. To overcome this drawback a frequency based demosaicing technique is proposed. The luminance and chrominance components extracted from the frequency domain of the image are interpolated to produce intermediate demosaiced images. A novel Neural Network Based Image Reconstruction Algorithm is applied to the intermediate demosaiced image to obtain resultant demosaiced images. The results presented in the paper prove the proposed demosaicing technique exhibits the best performance and is applicable to a wide variety of images

Color image demosaicing using sparse based radial basis function network

AbstractImages contain three primary colors at each pixel, but single sensor digital cameras capture only one of the primary channels. Process of color image reconstruction by finding the missing color component is called color image demosaicing. Various approaches have been proposed in this field of image demosaicing such as interpolation based and frequency based approaches due to sharp image edge and higher color saturation, and these techniques fail to reconstruct image efficiently. To overcome this, in this work we propose a new approach, sparse based RBF network for color image demosaicing. According to this approach a sparse model is constructed first and based on that weights are computed which are used to minimize the reconstruction error. To improve this we use optimal weight computation and RBF training for missing color component value prediction. Proposed method is implemented using MATLAB tool and experimental results show the efficiency of the proposed work in terms of color peak signal to noise ratio (CPSNR). Simulation results show 16.20% improvement in the performance in terms of CPSNR