In this term project, we proposed an new semi-supervised technique for image fill-in.A hierarchical Bayesian model is learnt from natural images and user specified image database, like face database if the image to be fill-in is a face.
Each layer is composed of a set of constellation model using the previous layer.At the first layer, for example, Gabor like image patches are learnt, higher layers are composed of several Gabors.
At each layer, MAP is used as patch detector and use this decision to guide the fill-in process from the top to bottom.
Such fill-in method can also be further developped to do: super resolution, occlusion detection, 3D from single image, etc.
similar related work by others: "Convolutional Deep
Belief Networks for Scalable Unsupervised Learning of Hierarchical Representations" H. Lee. et.al ICML 2009.
