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Open AccessArticle

Wavelet-Based Visible and Infrared Image Fusion: A Comparative Study

Facultad de Ingeniería en Electricidad y Computación, CIDIS, Escuela Superior Politécnica del Litoral, ESPOL, Campus Gustavo Galindo, Km 30.5 vía Perimetral, Guayaquil 09-01-5863, Ecuador
Computer Vision Center, Edifici O, Campus UAB, Bellaterra 08193, Barcelona, Spain
Computer Science Department, Universitat Autònoma de Barcelona, Campus UAB, Bellaterra 08193, Barcelona, Spain
Institute for Systems and Computer Engineering, Technology and Science, R. Dr. Roberto Frias 465, Porto 4200, Portugal
Institute of Electronics and Informatics Engineering of Aveiro, Campus Universitário de Santiago 3810-193 Aveiro, Portugal
Author to whom correspondence should be addressed.
Academic Editors: Vincenzo Spagnolo and Dragan Indjin
Sensors 2016, 16(6), 861;
Received: 25 January 2016 / Revised: 13 May 2016 / Accepted: 1 June 2016 / Published: 10 June 2016
(This article belongs to the Special Issue Infrared and THz Sensing and Imaging)
This paper evaluates different wavelet-based cross-spectral image fusion strategies adopted to merge visible and infrared images. The objective is to find the best setup independently of the evaluation metric used to measure the performance. Quantitative performance results are obtained with state of the art approaches together with adaptations proposed in the current work. The options evaluated in the current work result from the combination of different setups in the wavelet image decomposition stage together with different fusion strategies for the final merging stage that generates the resulting representation. Most of the approaches evaluate results according to the application for which they are intended for. Sometimes a human observer is selected to judge the quality of the obtained results. In the current work, quantitative values are considered in order to find correlations between setups and performance of obtained results; these correlations can be used to define a criteria for selecting the best fusion strategy for a given pair of cross-spectral images. The whole procedure is evaluated with a large set of correctly registered visible and infrared image pairs, including both Near InfraRed (NIR) and Long Wave InfraRed (LWIR). View Full-Text
Keywords: image fusion; fusion evaluation metrics; visible and infrared imaging; discrete wavelet transform image fusion; fusion evaluation metrics; visible and infrared imaging; discrete wavelet transform
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Sappa, A.D.; Carvajal, J.A.; Aguilera, C.A.; Oliveira, M.; Romero, D.; Vintimilla, B.X. Wavelet-Based Visible and Infrared Image Fusion: A Comparative Study. Sensors 2016, 16, 861.

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