Thermal Image Super-Resolution: A Novel Unsupervised Approach

Rivadeneira, Rafael E.; Sappa, Ángel D.; Vintimilla, Boris X.

doi:10.1007/978-3-030-94893-1_23

“…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%

Random Data Augmentation based Enhancement: A Generalized Enhancement Approach for Medical Datasets

Aleem¹,

Kumar²,

Little³

et al. 2022

24th Irish Machine Vision and Image Processing Conference

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Over the years, the paradigm of medical image analysis has shifted from manual expertise to automated systems, often using deep learning (DL) systems. The performance of deep learning algorithms is highly dependent on data quality. Particularly for the medical domain, it is an important aspect as medical data is very sensitive to quality and poor quality can lead to misdiagnosis. To improve the diagnostic performance, research has been done both in complex DL architectures and in improving data quality using dataset dependent static hyperparameters. However, the performance is still constrained due to data quality and overfitting of hyperparameters to a specific dataset. To overcome these issues, this paper proposes random data augmentation based enhancement. The main objective is to develop a generalized, data-independent and computationally efficient enhancement approach to improve medical data quality for DL. The quality is enhanced by improving the brightness and contrast of images. In contrast to the existing methods, our method generates enhancement hyperparameters randomly within a defined range, which makes it robust and prevents overfitting to a specific dataset. To evaluate the generalization of the proposed method, we use four medical datasets and compare its performance with state-of-the-art methods for both classification and segmentation tasks. For grayscale imagery, experiments have been performed with: COVID-19 chest X-ray, KiTS19, and for RGB imagery with: LC25000 datasets. Experimental results demonstrate that with the proposed enhancement methodology, DL architectures outperform other existing methods. Our code is publicly available at: https://github.com/aleemsidra/Augmentation-Based-Generalized-Enhancement.

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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

0

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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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“…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%

Triple Loss based Satellite Image Localisation for Aerial Platforms

Herrera¹,

McCarhy²,

McDonald³

2022

24th Irish Machine Vision and Image Processing Conference

0

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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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Thermal Image Super-Resolution: A Novel Unsupervised Approach

Cited by 18 publications

References 37 publications

Random Data Augmentation based Enhancement: A Generalized Enhancement Approach for Medical Datasets

Random Data Augmentation based Enhancement: A Generalized Enhancement Approach for Medical Datasets

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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