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Sparse representation based multi-sensor image fusion for multi-focus and multi-modality images:A review

Zhang, Qiang and Liu, Yi and Blum, Rick S. and Han, Jungong and Tao, Dacheng (2018) Sparse representation based multi-sensor image fusion for multi-focus and multi-modality images:A review. Information Fusion, 40. pp. 57-75. ISSN 1566-2535

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Abstract

As a result of several successful applications in computer vision and image processing, sparse representation (SR) has attracted significant attention in multi-sensor image fusion. Unlike the traditional multiscale transforms (MSTs) that presume the basis functions, SR learns an over-complete dictionary from a set of training images for image fusion, and it achieves more stable and meaningful representations of the source images. By doing so, the SR-based fusion methods generally outperform the traditional MST image fusion methods in both subjective and objective tests. In addition, they are less susceptible to mis-registration among the source images, thus facilitating the practical applications. This survey paper proposes a systematic review of the SR-based multi-sensor image fusion literature, highlighting the pros and cons of each category of approaches. Specifically, we start by performing a theoretical investigation of the entire system from three key algorithmic aspects, (1) sparse representation models; (2) dictionary learning methods; and (3) activity levels and fusion rules. Subsequently, we show how the existing works address these scientific problems and design the appropriate fusion rules for each application such as multi-focus image fusion and multi-modality (e.g., infrared and visible) image fusion. At last, we carry out some experiments to evaluate the impact of these three algorithmic components on the fusion performance when dealing with different applications. This article is expected to serve as a tutorial and source of reference for researchers preparing to enter the field or who desire to employ the sparse representation theory in other fields.

Item Type: Article
Journal or Publication Title: Information Fusion
Uncontrolled Keywords: Image fusion ; Sparse representation ; Dictionary learning ; Activity level
Subjects:
Departments: Faculty of Science and Technology > School of Computing & Communications
ID Code: 87039
Deposited By: ep_importer_pure
Deposited On: 13 Jul 2017 10:24
Refereed?: Yes
Published?: Published
Last Modified: 21 Nov 2017 20:00
Identification Number:
URI: http://eprints.lancs.ac.uk/id/eprint/87039

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