A patch-based framework for new ITK functionality: Joint fusion, denoising, and non-local super-resolution

Nicholas J. Tustison*,Brian Avants,Hongzhi Wang,Long Xie,Pierrick Coupe,Paul Yushkevich,Jose Manjon
Abstract
A patch-based framework for new ITK functionality: Joint fusion, denoising, and non-local super-resolution

Abstract

In an earlier Insight Journal article, we introduced an ITK implementation of the adaptive patch-based image denoising algorithm described in [3]. We follow-up up that offering with a generalized non-local, patch-based ITK class framework and a refactored denoising class. In addition, we provide two ITK implementations of related, well-known algorithms. The first is a non-local super resolution method described in [1, 2]. The second is the multivariate joint label fusion algorithm of [4, 5] with additional extensions, denoted as “joint intensity fusion”, which will be described in a forthcoming manuscript. Accompanying these ITK classes are documented programming interfaces which use our previously introduced unique command line interface routines. Several 2-D examples on brain imaging data are provided to qualitatively demonstrate performance.

Keywords

joint fusionPatch based image denoisingsuper-resolutionnon-local
Manuscript
Source Code and Data

Source Code and Data

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