ThermISRnet: an efficient thermal image super-resolution network

Patel, Heena; Chudasama, Vishal; Prajapati, Kalpesh; Upla, Kishor P.; Raja, Kiran B.; Ramachandra, Raghavendra; Busch, Christoph

doi:10.1117/1.oe.60.7.073101

Cited by 20 publications

(18 citation statements)

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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: 98%

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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Section: Introductionmentioning

confidence: 98%

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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“…The deep back-projection network (DBPN) [Haris et al, 2018] was proposed for super-resolution of single visible images. Further improvements include up-sampling layers with a recurrent network [Haris et al, 2019], an unsupervised approach using CycleGAN [Rivadeneira et al, 2020], Spatio-Temporal feature fusion deep neural network , with different up-sampling and asymmetrical residual learning in the network [Patel et al, 2021]. Considering thermal image super-resolution is an open research topic, a CVPR workshop also included a thermal super-resolution challenge.…”

Section: Introductionmentioning

confidence: 99%

Geometrically reconstructing confocal microscopy images for modelling the retinal microvasculature as a 3D cylindrical network

Troendle¹,

Barabás²,

Curtis³

2022

24th Irish Machine Vision and Image Processing Conference

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Microvascular networks can be modelled as a network of connected cylinders. Presently, however, there are limited approaches with which to recover these networks from biomedical images. We have therefore developed and implemented computer algorithms to geometrically reconstruct three-dimensional (3D) retinal microvascular networks from micrometre-scale imagery, resulting in a concise representation of two endpoints and radius for each cylinder detected within a delimited text file. This format is suitable for a variety of purposes, including efficient simulations of molecular delivery. Here, we detail a semi-automated pipeline consisting of the detection of retinal microvascular volumes within 3D imaging datasets, the enhancement and analysis of these volumes for reconstruction, and the geometric construction algorithm itself, which converts voxel data into representative 3D cylindrical objects.

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Section: Introductionmentioning

confidence: 99%

Triple Loss based Satellite Image Localisation for Aerial Platforms

Herrera¹,

McCarhy²,

McDonald³

2022

24th Irish Machine Vision and Image Processing Conference

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We present a vision-based technique for aerial platform localisation using satellite imagery. Our approach applies a modified VGG16 network in conjunction with a triplet loss to encode aerial views as discriminative scene embeddings. The platform is localised by comparing the encodding of its current view with a database of pre-encoded embeddings using a cosine similarity metric. Recent image-based localisation research has shown potential for such learned embeddings, however, to ensure reliable matching they require dense sampling of views of the environment, thereby limiting their operational area. In contrast, the combination of our proposed architecture in conjunction with the triplet loss shows robustness over greater spatial shifts, reducing the need for dense sampling. We demonstrate these improvements through comparison with a state-of-the-art approach using simulated ground truth sequences derived from a real-world satellite dataset covering a 1.5km × 1km region in Karslruhe.

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ThermISRnet: an efficient thermal image super-resolution network

Cited by 20 publications

References 0 publications

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

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

Geometrically reconstructing confocal microscopy images for modelling the retinal microvasculature as a 3D cylindrical network

Triple Loss based Satellite Image Localisation for Aerial Platforms

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