Unsupervised Patch-Based Image Regularization and Representation

Abstract

AbstractA novel adaptive and patch-based approach is proposed for image regularization and representation. The method is unsupervised and based on a pointwise selection of small image patches of fixed size in the variable neighborhood of each pixel. The main idea is to associate with each pixel the weighted sum of data points within an adaptive neighborhood and to use image patches to take into account complex spatial interactions in images. In this paper, we consider the problem of the adaptive neighborhood selection in a manner that it balances the accuracy of the estimator and the stochastic error, at each spatial position. Moreover, we propose a practical algorithm with no hidden parameter for image regularization that uses no library of image patches and no training algorithm. The method is applied to both artificially corrupted and real images and the performance is very close, and in some cases even surpasses, to that of the best published denoising methods.KeywordsTexture SynthesisDenoising MethodAdaptive NeighborhoodImage RegularizationSmall Image PatchThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.