Demosaicking using artificial neural networks

The problem of color image enhancement and the specific case of color demosaicing which involves reconstruction of color images from sampled images, is an under-constrained problem. Using single-channel restoration techniques on each color- channel separately results in poorly reconstructed images. It has been shown that better results can be obtained by considering the cross-channel correlation. In this paper, a novel approach to demosaicing is presented, using learning schemes based on Artificial Neural Networks. Thus the reconstruction parameters are determined specifically for predefined classes of images. This approach improves results for images of the learned class, since the variability of inputs is constrained (within the image class) and the parameters are robust due to the learning process. Three reconstruction methods are presented in this work. Additionally, a selection method is introduced, which combines several reconstruction methods and applies the best method for each input.