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Author
Angel Sappa; P. Carvajal; Cristhian A. Aguilera-Carrasco; Miguel Oliveira; Dennis Romero; Boris X. Vintimilla
Title
Wavelet based visible and infrared image fusion: a comparative study
Type
Journal Article
Year
2016
Publication
Sensors
Abbreviated Journal
SENS
Volume
16
Issue
6
Pages
1-15
Keywords
Image fusion; fusion evaluation metrics; visible and infrared imaging; discrete wavelet transform
Abstract
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).
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Series Volume
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Notes
ADAS; 600.086; 600.076
Approved
no
Call Number
Admin @ si @SCA2016
Serial
2807
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Author
Xavier Soria; Angel Sappa; Riad I. Hammoud
Title
Wide-Band Color Imagery Restoration for RGB-NIR Single Sensor Images
Type
Journal Article
Year
2018
Publication
Sensors
Abbreviated Journal
SENS
Volume
18
Issue
7
Pages
2059
Keywords
RGB-NIR sensor; multispectral imaging; deep learning; CNNs
Abstract
Multi-spectral RGB-NIR sensors have become ubiquitous in recent years. These sensors allow the visible and near-infrared spectral bands of a given scene to be captured at the same time. With such cameras, the acquired imagery has a compromised RGB color representation due to near-infrared bands (700–1100 nm) cross-talking with the visible bands (400–700 nm).
This paper proposes two deep learning-based architectures to recover the full RGB color images, thus removing the NIR information from the visible bands. The proposed approaches directly restore the high-resolution RGB image by means of convolutional neural networks. They are evaluated with several outdoor images; both architectures reach a similar performance when evaluated in different
scenarios and using different similarity metrics. Both of them improve the state of the art approaches.
Address
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ISBN
Medium
Area
Expedition
Conference
Notes
ADAS; MSIAU; 600.086; 600.130; 600.122; 600.118
Approved
no
Call Number
Admin @ si @ SSH2018
Serial
3145
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