Abstract
Image fusion provides a mechanism to combine multiple images into a single representation to aid human visual perception and image processing tasks. Such algorithms endeavour to create a fused image containing the salient information from each source image, without introducing artefacts or inconsistencies. Image fusion is applicable for numerous fields including: defence systems, remote sensing and geoscience, robotics and industrial engineering, and medical imaging. In the medical imaging domain, image fusion may aid diagnosis and surgical planning tasks requiring the segmentation, feature extraction, and/or visualisation of multi-modal datasets.This paper discusses the implementation of an image fusion toolkit built upon the Insight Toolkit (ITK). Based on an existing architecture, the proposed framework (GIFT) offers a ‘plug-and-play’ environment for the construction of n-D multi-scale image fusion methods. We give a brief overview of the toolkit design and demonstrate how to construct image fusion algorithms from low-level components (such as multi-scale methods and feature generators). A number of worked examples for medical applications are presented in Appendix A, including quadrature mirror filter discrete wavelet transform (QMF DWT) image fusion.
Highlights
Image fusion algorithms seek to combine multiple input images into a single representation
This paper presents an open-source toolkit for multi-scale image fusion and aims to be the start of a central repository for fusion algorithm implementations
Generalised Image Fusion Toolkit (GIFT) is built upon the Insight Toolkit
Summary
Image fusion algorithms seek to combine multiple input images into a single representation. The section discusses the design and currently implemented components of GIFT Following this we give a brief of list of instructions for getting started using the toolkit. Multi-sensor fusion has been organised into a hierarchy of increasing abstract tasks [5]: Signal-level is the lowest form of multi-sensor fusion At this level multiple raw input signals are fused into a single output signal. These algorithms can be grouped into the following categories: Arithmetic image fusion is a simple and efficient scheme, which suffers from loss of contrast due to the destructive nature of superposition This type of fusion applies a weight (wn) to each input image (in) and arithmetically combines these to form the output image ( f ), as shown below:. GIFT is integrated with ITK’s rich set of registration methods an essential first step for image fusion
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