Edge focused super-resolution of thermal images

Zoetgnande, Yannick Wend Kuni; Dillenseger, Jean-Louis; Alirezaie, Javad

doi:10.1109/ijcnn.2019.8852320

Cited by 9 publications

(5 citation statements)

References 33 publications

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“…Related work includes as well CDN-MRF [14], which introduces a cascaded architecture composed of two consecutive deep networks with different receptive fields that are jointly trained to increase the spatial resolution of thermal image by a large scale factor. Finally, in [15], an edge-focused method is proposed. It consists of a model based on residual dense blocks, that can perform super-resolution for thermal images, while enhancing the visual information of edges.…”

Section: A Thermal Image Enhancementmentioning

confidence: 99%

Thermal Image Enhancement using Generative Adversarial Network for Pedestrian Detection

Marnissi

Fradi

Sahbani³

et al. 2021

2020 25th International Conference on Pattern Recognition (ICPR)

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Infrared imaging has recently played an important role in a wide range of applications including video surveillance, robotics and night vision. However, infrared cameras often suffer from some limitations, essentially about low-contrast and blurred details. These problems contribute to the loss of observation of target objects in infrared images, which could limit the feasibility of different infrared imaging applications. In this paper, we mainly focus on the problem of pedestrian detection on thermal images. Particularly, we emphasis the need for enhancing the visual quality of images before performing the detection step.To address that, we propose a novel thermal enhancement architecture called TE-GAN based on Generative Adversarial Network, and composed of two modules contrast enhancement and denoising with a post-processing step for edge restoration in order to improve the overall image quality. The effectiveness of the proposed architecture is assessed by means of visual quality metrics and better results are obtained compared to the original thermal images and to the obtained results by other existing enhancement methods. These results have been conducted on a subset of KAIST dataset that we make available to encourage research in this direction 1 . Using the same dataset, the impact of the proposed enhancement architecture has been demonstrated on the detection results by obtaining better performance with a significant margin using YOLOv3 detector.

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Section: A Thermal Image Enhancementmentioning

confidence: 99%

Thermal Image Enhancement using Generative Adversarial Network for Pedestrian Detection

Marnissi

Fradi

Sahbani³

et al. 2021

2020 25th International Conference on Pattern Recognition (ICPR)

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“…The Computer Vision researchers are increasingly showing research interests in use of thermal images for a variety of applications [3][4][5]. Similarly, the same research trend is being noticed in SR applications using thermal images [6][7][8][9]. Rivadeneira et al proposed a Convolutional Neural Network (CNN) based approach to compare performance of Image SR using both thermal and visible images [10].…”

Section: Introductionmentioning

confidence: 97%

Performance Comparison of Deep Residual Networks-Based Super Resolution Algorithms Using Thermal Images: Case Study of Crowd Counting

Rizvi

Farooq

Raza

2022

Digital Interaction and Machine Intelligence

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Humans are able to perceive objects only in the visible spectrum range which limits the perception abilities in poor weather or low illumination conditions. The limitations are usually handled through technological advancements in thermographic imaging. However, thermal cameras have poor spatial resolutions compared to RGB cameras. Super-resolution (SR) techniques are commonly used to improve the overall quality of low-resolution images. There has been a major shift of research among the Computer Vision researchers towards SR techniques particularly aimed for thermal images. This paper analyzes the performance of three deep learning-based state-of-the-art SR algorithms namely Enhanced Deep Super Resolution (EDSR), Residual Channel Attention Network (RCAN) and Residual Dense Network (RDN) on thermal images. The algorithms were trained from scratch for different upscaling factors of ×2 and ×4. The dataset was generated from two different thermal imaging sequences of BU-TIV benchmark. The sequences contain both sparse and highly dense type of crowds with a far field camera view. The trained models were then used to super-resolve unseen test images. The quantitative analysis of the test images was performed using common image quality metrics such as PSNR, SSIM and LPIPS, while qualitative analysis was provided by evaluating effectiveness of the algorithms for crowd counting application. After only 54 and 51 epochs of RCAN and RDN respectively, both approaches were able to output average scores of 37.878, 0.986, 0.0098 and 30.175, 0.945, 0.0636 for PSNR, SSIM and LPIPS respectively. The EDSR algorithm took the least computation time during both training and testing because of its simple architecture. This research proves that a reasonable accuracy can be achieved with fewer training epochs when an application-specific dataset is carefully selected.

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“…Despite the usefulness of these cameras in such situations, there are some limitations that have to be considered, essentially about the expensive cost of high-resolution ones. This could explain the fact that thermal data is usually of bad quality and is less available compared to visible one [6], [7], [8], [4].…”

Section: Introductionmentioning

confidence: 99%

Unsupervised thermal-to-visible domain adaptation method for pedestrian detection

Marnissi

Fradi

Sahbani³

et al. 2022

Pattern Recognition Letters

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Edge focused super-resolution of thermal images

Cited by 9 publications

References 33 publications

Thermal Image Enhancement using Generative Adversarial Network for Pedestrian Detection

Thermal Image Enhancement using Generative Adversarial Network for Pedestrian Detection

Performance Comparison of Deep Residual Networks-Based Super Resolution Algorithms Using Thermal Images: Case Study of Crowd Counting

Unsupervised thermal-to-visible domain adaptation method for pedestrian detection

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