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Author  |
Patricia L. Suarez; Angel D. Sappa; Boris X. Vintimilla; Riad I. Hammoud |
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Title |
Near InfraRed Imagery Colorization |
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2018 |
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25 th IEEE International Conference on Image Processing, ICIP 2018 |
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2237-2241 |
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This paper proposes a stacked conditional Generative
Adversarial Network-based method for Near InfraRed
(NIR) imagery colorization. We propose a variant architecture
of Generative Adversarial Network (GAN) that uses multiple
loss functions over a conditional probabilistic generative model.
We show that this new architecture/loss-function yields better
generalization and representation of the generated colored IR
images. The proposed approach is evaluated on a large test
dataset and compared to recent state of the art methods using
standard metrics.1
Index Terms—Convolutional Neural Networks (CNN), Generative
Adversarial Network (GAN), Infrared Imagery colorization. |
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gtsi @ user @ |
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81 |
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Author |
Patricia L. Suarez; Angel D. Sappa; Boris X. Vintimilla; Riad I. Hammoud |
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Title |
Image Vegetation Index through a Cycle Generative Adversarial Network |
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Conference Article |
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2019 |
Publication |
Conference on Computer Vision and Pattern Recognition Workshops (CVPR 2019); Long Beach, California, United States |
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1014-1021 |
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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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Call Number |
gtsi @ user @ |
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106 |
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Author |
Patricia Suarez & Angel D. Sappa |
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Title |
Haze-Free Imaging through Haze-Aware Transformer Adaptations |
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Year |
2024 |
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In Fourth International Conference on Innovations in Computational Intelligence and Computer Vision (ICICV 2024) |
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Call Number |
cidis @ cidis @ |
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236 |
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Author |
Patricia Suarez, Angel D. Sappa |
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Title |
A Generative Model for Guided Thermal Image Super-Resolution |
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2024 |
Publication |
In 19th International Conference on Computer Vision Theory and Applications VISAPP 2024 |
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Call Number |
cidis @ cidis @ |
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240 |
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Author |
Rafael E. Rivadeneira, Angel D. Sappa and Boris X. Vintimilla |
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Title |
Multi-Image Super-Resolution for Thermal Images. |
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Conference Article |
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2022 |
Publication |
Proceedings of the International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications VISIGRAPP 2022 |
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4 |
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635 - 642 |
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Call Number |
cidis @ cidis @ |
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181 |
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Author |
Rafael E. Rivadeneira, Angel D. Sappa, Boris X. Vintimilla, Chenyang Wang, Junjun Jiang, Xianming Liu, Zhiwei Zhong, Dai Bin, Li Ruodi, Li Shengye |
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Title |
Thermal Image Super-Resolution Challenge Results – PBVS 2023 |
Type |
Conference Article |
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Year |
2023 |
Publication |
19th IEEE Workshop on Perception Beyond the Visible Spectrum de la Conferencia Computer Vision & Pattern Recognition CVPR 2023, junio 18-28 |
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2023-June |
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470 - 478 |
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21607508 |
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979-835030249-3 |
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Call Number |
cidis @ cidis @ |
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210 |
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Author |
Rafael E. Rivadeneira, Angel D. Sappa, Boris X. Vintimilla, Jin Kim, Dogun Kim et al. |
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Title |
Thermal Image Super-Resolution Challenge Results- PBVS 2022. |
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Conference Article |
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Year |
2022 |
Publication |
Computer Vision and Pattern Recognition Workshops, (CVPRW 2022), junio 19-24. |
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CONFERENCE |
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2022-June |
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349-357 |
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This paper presents results from the third Thermal Image
Super-Resolution (TISR) challenge organized in the Perception Beyond the Visible Spectrum (PBVS) 2022 workshop.
The challenge uses the same thermal image dataset as the
first two challenges, with 951 training images and 50 validation images at each resolution. A set of 20 images was
kept aside for testing. The evaluation tasks were to measure
the PSNR and SSIM between the SR image and the ground
truth (HR thermal noisy image downsampled by four), and
also to measure the PSNR and SSIM between the SR image
and the semi-registered HR image (acquired with another
camera). The results outperformed those from last year’s
challenge, improving both evaluation metrics. This year,
almost 100 teams participants registered for the challenge,
showing the community’s interest in this hot topic. |
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Call Number |
cidis @ cidis @ |
Serial |
175 |
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Author |
Rafael E. Rivadeneira, Angel D. Sappa, Chenyang Wang, Junjun Jiang, Zhiwei Zhong, Peilin Chen & Shiqi Wang |
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Title |
Thermal Image Super Resolution Challenge Results – PBVS 2024 |
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Conference Article |
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2024 |
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Accepted in 20th IEEE Workshop on Perception Beyond the Visible Spectrum of the 2024 Conference on Computer Vision and Pattern Recognition |
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Call Number |
cidis @ cidis @ |
Serial |
233 |
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Author |
Rafael E. Rivadeneira, Henry O. Velesaca, Angel D. Sappa |
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Title |
Object Detection in Very Low-Resolution Thermal Images through a Guided-Based Super-Resolution Approach |
Type |
Conference Article |
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2023 |
Publication |
17th International Conference On Signal Image Technology & Internet Based System |
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Call Number |
cidis @ cidis @ |
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224 |
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Author |
Rafael E. Rivadeneira; Angel D. Sappa; Boris X. Vintimilla |
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Title |
Thermal Image Super-Resolution: a Novel Architecture and Dataset |
Type |
Conference Article |
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Year |
2020 |
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The 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2020); Valletta, Malta; 27-29 Febrero 2020 |
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4 |
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111-119 |
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Keywords |
Thermal images, Far Infrared, Dataset, Super-Resolution. |
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Abstract |
This paper proposes a novel CycleGAN architecture for thermal image super-resolution, together with a large
dataset consisting of thermal images at different resolutions. The dataset has been acquired using three thermal
cameras at different resolutions, which acquire images from the same scenario at the same time. The thermal
cameras are mounted in rig trying to minimize the baseline distance to make easier the registration problem.
The proposed architecture is based on ResNet6 as a Generator and PatchGAN as Discriminator. The novelty
on the proposed unsupervised super-resolution training (CycleGAN) is possible due to the existence of aforementioned thermal images—images of the same scenario with different resolutions. The proposed approach
is evaluated in the dataset and compared with classical bicubic interpolation. The dataset and the network are
available. |
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978-989758402-2 |
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gtsi @ user @ |
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121 |
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