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Title: Thermographic image super-resolution based on neural networks
Authors: Galván Hernández, Antonio David 
Ticay Rivas, Jaime Roberto 
Alonso Eugenio, Víctor 
Araña Pulido, Víctor Alexis 
Cabrera Almeida, Francisco José 
UNESCO Clasification: 3325 Tecnología de las telecomunicaciones
Issue Date: 2022
Project: Geolocalización aUtomatizada de Incendios forestales mediante Red sostenible de sensoRes de bajo coste y fácil dEspliegue 
Red integral de prevención y Gestión de incendios Forestales mediante Georreferenciación en Observadores móviles (Red_GesFoGO) 
Journal: 2022 3Rd Ursi Atlantic And Asia Pacific Radio Science Meeting, At-Ap-Rasc 2022
Conference: 3rd URSI Atlantic Radio Science Meeting (AT-AP-RASC 2022) 
Abstract: The continuous development of thermographic technology has led to the overall improvement of instruments used in medicine, surveillance, or military systems. However, thermographic imaging cameras still have a high cost compared to other alternatives on the market, such as visible light cameras and a much lower spatial resolution. Superresolution is a technique that improves the visual quality of an image through software processing. This work studies three neural networks architectures based on deep learning capable of performing super-resolution of RGB images at x2 and x4 scales: Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network (SRGAN), Enhanced Deep Residual Networks for Single Image Super-Resolution (EDSR), and Wide Activation for Efficient and Accurate Image Super-Resolution (WDSR). These architectures, in this work, have been trained as a super-resolution system using thermographic images as input data. The evaluation was carried out using thermographic images from different thermographic cameras. The performance assessment was carried out using the Peak Signal to Noise Ratio (PSNR) and the Structural Similarity Index Measure (SSIM). In addition, low-resolution images from a low-cost thermographic camera were used as input to the neural networks to study the feasibility of this method.
ISBN: 978-9-4639-6-8058
DOI: 10.23919/AT-AP-RASC54737.2022.9814417
Source: 2022 3rd URSI Atlantic and Asia Pacific Radio Science Meeting (AT-AP-RASC)
Appears in Collections:Actas de congresos
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