Image hallucination at different times of day using locally affine model and kNN template matching from time-lapse images

Image Hallucination has many applications in areas such as image processing, computational photography and image fusion. In this paper, we present an image Hallucination technique based on the template (patch) matching from the database of time lapse images and learned locally affine model. Template based techniques suffer from blocky artifacts. So, we propose two approaches for imposing consistency criteria across neighbouring patches in the form of regularization. We validate our Color transfer technique by hallucinating a variety of natural images at different times the day. We compare the proposed approach with other state of the art techniques of example image based color transfer and show that the images obtained using our approach look more plausible and natural.