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Author | Rafael E. Rivadeneira; Angel D. Sappa; Boris X. Vintimilla | ||||
Title | Thermal Image Super-Resolution: a Novel Architecture and Dataset | Type | Conference Article | ||
Year | 2020 | Publication | The 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2020); Valletta, Malta; 27-29 Febrero 2020 | Abbreviated Journal | |
Volume | 4 | Issue | Pages | 111-119 | |
Keywords | Thermal images, Far Infrared, Dataset, Super-Resolution. | ||||
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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ISSN | ISBN | 978-989758402-2 | Medium | ||
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Notes | Approved | no | |||
Call Number | gtsi @ user @ | Serial | 121 | ||
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Author | Rafael E. Rivadeneira; Angel D. Sappa; Boris X. Vintimilla; Lin Guo; Jiankun Hou; Armin Mehri; Parichehr Behjati; Ardakani Heena Patel; Vishal Chudasama; Kalpesh Prajapati; Kishor P. Upla; Raghavendra Ramachandra; Kiran Raja; Christoph Busch; Feras Almasri; Olivier Debeir; Sabari Nathan; Priya Kansal; Nolan Gutierrez; Bardia Mojra; William J. Beksi | ||||
Title | Thermal Image Super-Resolution Challenge – PBVS 2020 | Type | Conference Article | ||
Year | 2020 | Publication | The 16th IEEE Workshop on Perception Beyond the Visible Spectrum on the Conference on Computer Vision and Pattern Recongnition (CVPR 2020) | Abbreviated Journal | |
Volume | 2020-June | Issue | 9151059 | Pages | 432-439 |
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Abstract | This paper summarizes the top contributions to the first challenge on thermal image super-resolution (TISR) which was organized as part of the Perception Beyond the Visible Spectrum (PBVS) 2020 workshop. In this challenge, a novel thermal image dataset is considered together with stateof-the-art approaches evaluated under a common framework. The dataset used in the challenge consists of 1021 thermal images, obtained from three distinct thermal cameras at different resolutions (low-resolution, mid-resolution, and high-resolution), resulting in a total of 3063 thermal images. From each resolution, 951 images are used for training and 50 for testing while the 20 remaining images are used for two proposed evaluations. The first evaluation consists of downsampling the low-resolution, midresolution, and high-resolution thermal images by x2, x3 and x4 respectively, and comparing their super-resolution results with the corresponding ground truth images. The second evaluation is comprised of obtaining the x2 superresolution from a given mid-resolution thermal image and comparing it with the corresponding semi-registered highresolution thermal image. Out of 51 registered participants, 6 teams reached the final validation phase. |
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Language | English | Summary Language | Original Title | ||
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ISSN | 21607508 | ISBN | 978-172819360-1 | Medium | |
Area | Expedition | Conference | |||
Notes | Approved | no | |||
Call Number | cidis @ cidis @ | Serial | 123 | ||
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Author | Rafael E. Rivadeneira; Patricia L. Suarez; Angel D. Sappa; Boris X. Vintimilla. | ||||
Title | Thermal Image SuperResolution through Deep Convolutional Neural Network. | Type | Conference Article | ||
Year | 2019 | Publication | 16th International Conference on Image Analysis and Recognition (ICIAR 2019); Waterloo, Canadá | Abbreviated Journal | |
Volume | Issue | Pages | 417-426 | ||
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Abstract | Due to the lack of thermal image datasets, a new dataset has been acquired for proposed a superesolution approach using a Deep Convolution Neural Network schema. In order to achieve this image enhancement process a new thermal images dataset is used. Di?erent experiments have been carried out, ?rstly, the proposed architecture has been trained using only images of the visible spectrum, and later it has been trained with images of the thermal spectrum, the results showed that with the network trained with thermal images, better results are obtained in the process of enhancing the images, maintaining the image details and perspective. The thermal dataset is available at http://www.cidis.espol.edu.ec/es/dataset | ||||
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Notes | Approved | no | |||
Call Number | gtsi @ user @ | Serial | 103 | ||
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Author | Ricaurte P; Chilán C; Cristhian A. Aguilera; Boris X. Vintimilla; Angel D. Sappa | ||||
Title | Feature Point Descriptors: Infrared and Visible Spectra | Type | Journal Article | ||
Year | 2014 | Publication | Sensors Journal | Abbreviated Journal | |
Volume | Vol. 14 | Issue | Pages | pp. 3690-3701 | |
Keywords | cross-spectral imaging; feature point descriptors | ||||
Abstract | This manuscript evaluates the behavior of classical feature point descriptors when they are used in images from long-wave infrared spectral band and compare them with the results obtained in the visible spectrum. Robustness to changes in rotation, scaling, blur, and additive noise are analyzed using a state of the art framework. Experimental results using a cross-spectral outdoor image data set are presented and conclusions from these experiments are given. | ||||
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Language | English | Summary Language | English | Original Title | |
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Notes | Approved | no | |||
Call Number | cidis @ cidis @ | Serial | 28 | ||
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Author | Sianna Puente; Cindy Madrid; Miguel Realpe; Boris X. Vintimilla | ||||
Title | An Empirical Comparison of DCNN libraries to implement the Vision Module of a Danger Management System | Type | Conference Article | ||
Year | 2017 | Publication | 2017 International Conference on Deep Learning Technologies (ICDLT 2017) | Abbreviated Journal | |
Volume | Part F128535 | Issue | Pages | 60-65 | |
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Call Number | cidis @ cidis @ | Serial | 56 | ||
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