Partially supervised neighbor embedding for example-based image super-resolution

Publication Type:
Journal Article
IEEE Journal on Selected Topics in Signal Processing, 2011, 5 (2), pp. 230 - 239
Issue Date:
Filename Description Size
Thumbnail2011000271OK.pdf1.66 MB
Adobe PDF
Full metadata record
Neighbor embedding algorithm has been widely used in example-based super-resolution reconstruction from a single frame, which makes the assumption that neighbor patches embedded are contained in a single manifold. However, it is not always true for complicated texture structure. In this paper, we believe that textures may be contained in multiple manifolds, corresponding to classes. Under this assumption, we present a novel example-based image super-resolution reconstruction algorithm with clustering and supervised neighbor embedding (CSNE). First, a class predictor for low-resolution (LR) patches is learnt by an unsupervised Gaussian mixture model. Then by utilizing class label information of each patch, a supervised neighbor embedding is used to estimate high-resolution (HR) patches corresponding to LR patches. The experimental results show that the proposed method can achieve a better recovery of LR comparing with other simple schemes using neighbor embedding. © 2010 IEEE.
Please use this identifier to cite or link to this item: