A Guided Upsampling Network for Short Wave Infrared Images Using Graph Regularization
ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)(2023)
摘要
Exploiting the infrared area of the spectrum for classification problems is
getting increasingly popular, because many materials have characteristic
absorption bands in this area. However, sensors in the short wave infrared
(SWIR) area and even higher wavelengths have a very low spatial resolution in
comparison to classical cameras that operate in the visible wavelength area.
Thus, in this paper an upsampling method for SWIR images guided by a visible
image is presented. For that, the proposed guided upsampling network (GUNet)
uses a graph-regularized optimization problem based on learned affinities is
presented. The evaluation is based on a novel synthetic near-field visible-SWIR
stereo database. Different guided upsampling methods are evaluated, which shows
an improvement of nearly 1 dB on this database for the proposed upsampling
method in comparison to the second best guided upsampling network. Furthermore,
a visual example of an upsampled SWIR image of a real-world scene is depicted
for showing real-world applicability.
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关键词
Image Processing,Deep Learning,Short Wave Infrared Imaging,Guided Upsampling
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