Geometry-Aware Neighborhood Search for Learning Local Models for Image Superresolution

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
Julio Cesar FerreiraChristine Guillemot

Abstract

Local learning of sparse image models has proved to be very effective to solve inverse problems in many computer vision applications. To learn such models, the data samples are often clustered using the K-means algorithm with the Euclidean distance as a dissimilarity metric. However, the Euclidean distance may not always be a good dissimilarity measure for comparing data samples lying on a manifold. In this paper, we propose two algorithms for determining a local subset of training samples from which a good local model can be computed for reconstructing a given input test sample, where we consider the underlying geometry of the data. The first algorithm, called adaptive geometry-driven nearest neighbor search (AGNN), is an adaptive scheme, which can be seen as an out-of-sample extension of the replicator graph clustering method for local model learning. The second method, called geometry-driven overlapping clusters (GOCs), is a less complex nonadaptive alternative for training subset selection. The proposed AGNN and GOC methods are evaluated in image superresolution and shown to outperform spectral clustering, soft clustering, and geodesic distance-based subset selection in most settings.

References

Sep 21, 2004·IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society·Zhou WangEero P Simoncelli
Dec 13, 2006·IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society·Michael Elad, Michal Aharon
May 21, 2010·IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society·Jianchao YangYi Ma
Feb 1, 2011·IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society·Weisheng DongXiaolin Wu
Feb 5, 2011·IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society·Lin ZhangDavid Zhang
Apr 27, 2011·IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society·Jie NiRama Chellappa
Dec 20, 2011·IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society·Guoshen YuStéphane Mallat
Dec 28, 2012·IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society·Weisheng DongXin Li
May 13, 2014·IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society·Tomer Peleg, Michael Elad

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