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Author
Armin Mehri
;
Angel D. Sappa
Title
Colorizing Near Infrared Images through a Cyclic Adversarial Approach of Unpaired Samples
Type
Conference Article
Year
2019
Publication
Conference on Computer Vision and Pattern Recognition Workshops (CVPR 2019); Long Beach, California, United States
Abbreviated Journal
Volume
Issue
Pages
971-979
Keywords
Abstract
This paper presents a novel approach for colorizing
near infrared (NIR) images. The approach is based on
image-to-image translation using a Cycle-Consistent adversarial network for learning the color channels on unpaired dataset. This architecture is able to handle unpaired datasets. The approach uses as generators tailored
networks that require less computation times, converge
faster and generate high quality samples. The obtained results have been quantitatively—using standard evaluation
metrics—and qualitatively evaluated showing considerable
improvements with respect to the state of the art
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no
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gtsi @ user @
Serial
105
Permanent link to this record
Author
Patricia L. Suarez
;
Angel D. Sappa
;
Boris X. Vintimilla
;
Riad I. Hammoud
Title
Image Vegetation Index through a Cycle Generative Adversarial Network
Type
Conference Article
Year
2019
Publication
Conference on Computer Vision and Pattern Recognition Workshops (CVPR 2019); Long Beach, California, United States
Abbreviated Journal
Volume
Issue
Pages
1014-1021
Keywords
Abstract
This paper proposes a novel approach to estimate the
Normalized Difference Vegetation Index (NDVI) just from
an RGB image. The NDVI values are obtained by using
images from the visible spectral band together with a synthetic near infrared image obtained by a cycled GAN. The
cycled GAN network is able to obtain a NIR image from
a given gray scale image. It is trained by using unpaired
set of gray scale and NIR images by using a U-net architecture and a multiple loss function (gray scale images are
obtained from the provided RGB images). Then, the NIR
image estimated with the proposed cycle generative adversarial network is used to compute the NDVI index. Experimental results are provided showing the validity of the proposed approach. Additionally, comparisons with previous
approaches are also provided.
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no
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gtsi @ user @
Serial
106
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