scholarly journals Super Resolution Infrared Thermal Imaging Using Pansharpening Algorithms: Quantitative Assessment and Application to UAV Thermal Imaging

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1265
Author(s):  
Javier Raimundo ◽  
Serafin Lopez-Cuervo Medina ◽  
Juan F. Prieto ◽  
Julian Aguirre de Mata
Keyword(s):  
High Resolution ◽  
Thermal Imaging ◽  
Super Resolution ◽  
Limiting Factor ◽  
Algorithm Selection ◽  
Infrared Thermal Imaging ◽  
Thermal Images ◽  
Remote Sensors ◽  
Satellite Sensors ◽  
High Resolution Images

The lack of high-resolution thermal images is a limiting factor in the fusion with other sensors with a higher resolution. Different families of algorithms have been designed in the field of remote sensors to fuse panchromatic images with multispectral images from satellite platforms, in a process known as pansharpening. Attempts have been made to transfer these pansharpening algorithms to thermal images in the case of satellite sensors. Our work analyses the potential of these algorithms when applied to thermal images from unmanned aerial vehicles (UAVs). We present a comparison, by means of a quantitative procedure, of these pansharpening methods in satellite images when they are applied to fuse high-resolution images with thermal images obtained from UAVs, in order to be able to choose the method that offers the best quantitative results. This analysis, which allows the objective selection of which method to use with this type of images, has not been done until now. This algorithm selection is used here to fuse images from thermal sensors on UAVs with other images from different sensors for the documentation of heritage, but it has applications in many other fields.

scholarly journals Deep Learning Based Super Resolution and Classification Applications for Neonatal Thermal Images

2021 ◽  
Vol 38 (5) ◽  
pp. 1361-1368
Author(s):  
Fatih M. Senalp ◽  
Murat Ceylan
Keyword(s):  
Deep Learning ◽  
High Resolution ◽  
Thermal Imaging ◽  
Similarity Index ◽  
Super Resolution ◽  
Ground Truth ◽  
Structural Similarity ◽  
Generative Adversarial Networks ◽  
Thermal Camera ◽  
Thermal Images

The thermal camera systems can be used in all kinds of applications that require the detection of heat change, but thermal imaging systems are highly costly systems. In recent years, developments in the field of deep learning have increased the success by obtaining quality results compared to traditional methods. In this paper, thermal images of neonates (healthy - unhealthy) obtained from a high-resolution thermal camera were used and these images were evaluated as high resolution (ground truth) images. Later, these thermal images were downscaled at 1/2, 1/4, 1/8 ratios, and three different datasets consisting of low-resolution images in different sizes were obtained. In this way, super-resolution applications have been carried out on the deep network model developed based on generative adversarial networks (GAN) by using three different datasets. The successful performance of the results was evaluated with PSNR (peak signal to noise ratio) and SSIM (structural similarity index measure). In addition, healthy - unhealthy classification application was carried out by means of a classifier network developed based on convolutional neural networks (CNN) to evaluate the super-resolution images obtained using different datasets. The obtained results show the importance of combining medical thermal imaging with super-resolution methods.

scholarly journals Accelerating Super-Resolution and Visual Task Analysis in Medical Images

2020 ◽  
Vol 10 (12) ◽  
pp. 4282
Author(s):  
Ghada Zamzmi ◽  
Sivaramakrishnan Rajaraman ◽  
Sameer Antani
Keyword(s):  
Deep Learning ◽  
High Resolution ◽  
Task Analysis ◽  
Multiple Scales ◽  
Medical Images ◽  
Computational Cost ◽  
Super Resolution ◽  
Visual Task ◽  
Learning Networks ◽  
High Resolution Images

Medical images are acquired at different resolutions based on clinical goals or available technology. In general, however, high-resolution images with fine structural details are preferred for visual task analysis. Recognizing this significance, several deep learning networks have been proposed to enhance medical images for reliable automated interpretation. These deep networks are often computationally complex and require a massive number of parameters, which restrict them to highly capable computing platforms with large memory banks. In this paper, we propose an efficient deep learning approach, called Hydra, which simultaneously reduces computational complexity and improves performance. The Hydra consists of a trunk and several computing heads. The trunk is a super-resolution model that learns the mapping from low-resolution to high-resolution images. It has a simple architecture that is trained using multiple scales at once to minimize a proposed learning-loss function. We also propose to append multiple task-specific heads to the trained Hydra trunk for simultaneous learning of multiple visual tasks in medical images. The Hydra is evaluated on publicly available chest X-ray image collections to perform image enhancement, lung segmentation, and abnormality classification. Our experimental results support our claims and demonstrate that the proposed approach can improve the performance of super-resolution and visual task analysis in medical images at a remarkably reduced computational cost.

scholarly journals Micrometer-resolution imaging using MÖNCH: towards G2-less grating interferometry

2016 ◽  
Vol 23 (6) ◽  
pp. 1462-1473 ◽  
Author(s):  
Sebastian Cartier ◽  
Matias Kagias ◽  
Anna Bergamaschi ◽  
Zhentian Wang ◽  
Roberto Dinapoli ◽  
...  
Keyword(s):  
High Resolution ◽  
Silicon Detector ◽  
Limiting Factor ◽  
X Rays ◽  
X Ray ◽  
Charge Cloud ◽  
High Resolution Images ◽  
Resolution Imaging ◽  
Hybrid Silicon ◽  
Small Pixel

MÖNCH is a 25 µm-pitch charge-integrating detector aimed at exploring the limits of current hybrid silicon detector technology. The small pixel size makes it ideal for high-resolution imaging. With an electronic noise of about 110 eV r.m.s., it opens new perspectives for many synchrotron applications where currently the detector is the limiting factor,e.g.inelastic X-ray scattering, Laue diffraction and soft X-ray or high-resolution color imaging. Due to the small pixel pitch, the charge cloud generated by absorbed X-rays is shared between neighboring pixels for most of the photons. Therefore, at low photon fluxes, interpolation algorithms can be applied to determine the absorption position of each photon with a resolution of the order of 1 µm. In this work, the characterization results of one of the MÖNCH prototypes are presented under low-flux conditions. A custom interpolation algorithm is described and applied to the data to obtain high-resolution images. Images obtained in grating interferometry experiments without the use of the absorption grating G2are shown and discussed. Perspectives for the future developments of the MÖNCH detector are also presented.

scholarly journals Semi-Coupled Convolutional Sparse Learning for Image Super-Resolution

2019 ◽  
Vol 11 (21) ◽  
pp. 2593
Author(s):  
Li ◽  
Zhang ◽  
Jiao ◽  
Liu ◽  
Yang ◽  
...  
Keyword(s):  
High Resolution ◽  
Super Resolution ◽  
Mapping Function ◽  
Linear Mapping ◽  
Sparse Learning ◽  
Proposed Model ◽  
Filter Size ◽  
High Resolution Images ◽  
Resolution Problem ◽  
Image Super Resolution

In the convolutional sparse coding-based image super-resolution problem, the coefficients of low- and high-resolution images in the same position are assumed to be equivalent, which enforces an identical structure of low- and high-resolution images. However, in fact the structure of high-resolution images is much more complicated than that of low-resolution images. In order to reduce the coupling between low- and high-resolution representations, a semi-coupled convolutional sparse learning method (SCCSL) is proposed for image super-resolution. The proposed method uses nonlinear convolution operations as the mapping function between low- and high-resolution features, and conventional linear mapping can be seen as a special case of the proposed method. Secondly, the neighborhoods within the filter size are used to calculate the current pixel, improving the flexibility of our proposed model. In addition, the filter size is adjustable. In order to illustrate the effectiveness of SCCSL method, we compare it with four state-of-the-art methods of 15 commonly used images. Experimental results show that this work provides a more flexible and efficient approach for image super-resolution problem.

scholarly journals Crop Disease Classification on Inadequate Low-Resolution Target Images

Sensors ◽  
2020 ◽  
Vol 20 (16) ◽  
pp. 4601
Author(s):  
Juan Wen ◽  
Yangjing Shi ◽  
Xiaoshi Zhou ◽  
Yiming Xue
Keyword(s):  
High Resolution ◽  
Super Resolution ◽  
Classification Performance ◽  
Disease Classification ◽  
Generative Adversarial Networks ◽  
Low Resolution ◽  
Training Samples ◽  
Crop Disease ◽  
High Resolution Images ◽  
Model Training

Currently, various agricultural image classification tasks are carried out on high-resolution images. However, in some cases, we cannot get enough high-resolution images for classification, which significantly affects classification performance. In this paper, we design a crop disease classification network based on Enhanced Super-Resolution Generative adversarial networks (ESRGAN) when only an insufficient number of low-resolution target images are available. First, ESRGAN is used to recover super-resolution crop images from low-resolution images. Transfer learning is applied in model training to compensate for the lack of training samples. Then, we test the performance of the generated super-resolution images in crop disease classification task. Extensive experiments show that using the fine-tuned ESRGAN model can recover realistic crop information and improve the accuracy of crop disease classification, compared with the other four image super-resolution methods.

scholarly journals Clinical Trial on the Characteristics of Zheng Classification of Pulmonary Diseases Based on Infrared Thermal Imaging Technology

2013 ◽  
Vol 2013 ◽  
pp. 1-6
Author(s):  
Jin-xia Ni ◽  
Si-hua Gao ◽  
Yu-hang Li ◽  
Shi-lei Ma ◽  
Tian Tian ◽  
...  
Keyword(s):  
Clinical Trial ◽  
Pulmonary Disease ◽  
Thermal Imaging ◽  
Relative Temperature ◽  
Infrared Thermal Imaging ◽  
Imaging Technology ◽  
Thermal Images ◽  
Diagnosis Rate ◽  
Infrared Thermal Images

Zheng classification study based on infrared thermal imaging technology has not been reported before. To detect the relative temperature of viscera and bowels of different syndromes patients with pulmonary disease and to summarize the characteristics of different Zheng classifications, the infrared thermal imaging technology was used in the clinical trial. The results showed that the infrared thermal images characteristics of different Zheng classifications of pulmonary disease were distinctly different. The influence on viscera and bowels was deeper in phlegm-heat obstructing lung syndrome group than in cold-phlegm obstructing lung syndrome group. It is helpful to diagnose Zheng classification and to improve the diagnosis rate by analyzing the infrared thermal images of patients. The application of infrared thermal imaging technology provided objective measures for medical diagnosis and treatment in the field of Zheng studies and provided a new methodology for Zheng classification.

Improving Scanning Electron Microscope Resolution for Near Planar Samples Through the Use of Image Restoration

2013 ◽  
Vol 20 (1) ◽  
pp. 78-89 ◽  
Author(s):  
Eric Lifshin ◽  
Yudhishthir P. Kandel ◽  
Richard L. Moore
Keyword(s):  
Electron Microscope ◽  
High Resolution ◽  
Scanning Electron Microscope ◽  
Accurate Determination ◽  
Limiting Factor ◽  
Beam Shape ◽  
Probe Diameter ◽  
High Resolution Images ◽  
Beam Currents ◽  
Scanning Electron

AbstractA method is presented for determining the point spread function (PSF) of an electron beam in a scanning electron microscope for the examination of near planar samples. Once measured, PSFs can be used with two or more low-resolution images of a selected area to create a high-resolution reconstructed image of that area. As an example, a 4× improvement in resolution for images is demonstrated for a fine gold particle sample. Since thermionic source instruments have high beam currents associated with large probe sizes, use of this approach implies that high-resolution images can be produced rapidly if the probe diameter is less of a limiting factor. Additionally, very accurate determination of the PSFs can lead to a better understanding of instrument performance as exemplified by very accurate measurement of the beam shape and therefore the degree of astigmatism.

scholarly journals Vehicle images reconstruction using SRCNN for improving the recognition accuracy of vehicle license plate number

2020 ◽  
Vol 8 (4) ◽  
pp. 304-310
Author(s):  
Windra Swastika ◽  
Ekky Rino Fajar Sakti ◽  
Mochamad Subianto
Keyword(s):  
High Resolution ◽  
Character Recognition ◽  
Recognition Accuracy ◽  
Super Resolution ◽  
Training Data ◽  
License Plate ◽  
Plate Number ◽  
Average Accuracy ◽  
Number Recognition ◽  
High Resolution Images

Low-resolution images can be reconstructed into high-resolution images using the Super-resolution Convolution Neural Network (SRCNN) algorithm. This study aims to improve the vehicle license plate number's recognition accuracy by generating a high-resolution vehicle image using the SRCNN. The recognition is carried out by two types of character recognition methods: Tesseract OCR and SPNet. The training data for SRCNN uses the DIV2K dataset consisting of 900 images, while the training data for character recognition uses the Chars74 dataset. The high-resolution images constructed using SRCNN can increase the average accuracy of vehicle license plate number recognition by 16.9 % using Tesseract and 13.8 % with SPNet.

scholarly journals Smartphone-based infrared thermal imaging for differentiating venomous snakebites from non-venomous and dry bites

2020 ◽  
Author(s):  
Paramasivam Sabitha ◽  
Chanaveerappa Bammigatti ◽  
Surendran Deepanjali ◽  
Bettadpura Shamanna Suryanarayana ◽  
Tamilarasu Kadhiravan
Keyword(s):  
Thermal Imaging ◽  
Infrared Imaging ◽  
Hot Spot ◽  
Body Part ◽  
Night Vision ◽  
Care Providers ◽  
Infrared Thermal Imaging ◽  
Thermal Images ◽  
Thermal Changes ◽  
Infrared Thermal Images

AbstractBackgroundLocal envenomation following snakebites is accompanied by thermal changes, which could be visualized using infrared imaging. We explored whether infrared thermal imaging could be used to differentiate venomous snakebites from non-venomous and dry bites.MethodsWe prospectively enrolled adult patients with a history of snakebite in the past 24 hours presenting to the emergency of a teaching hospital in southern India. A standardized clinical evaluation for symptoms and signs of envenomation including 20-minute whole-blood clotting test and prothrombin time was performed to assess envenomation status. Infrared thermal imaging was done at enrolment, 6 hours, and 24 hours using a smartphone-based device under ambient conditions. Processed infrared thermal images were independently interpreted twice by a reference rater and once by three novice raters.FindingsWe studied 89 patients; 60 (67%) of them were male. Median (IQR) time from bite to enrolment was 11 (6.5—15) hours; 21 (24%) patients were enrolled within 6 hours of snakebite. In all, 48 patients had local envenomation with/without systemic envenomation, and 35 patients were classified as non-venomous/dry bites. Envenomation status was unclear in six patients. At enrolment, area of increased temperature around the bite site (Hot spot) was evident on infrared thermal imaging in 45 of the 48 patients with envenomation, while hot spot was evident in only 6 of the 35 patients without envenomation. Presence of hot spot on baseline infrared thermal images had a sensitivity of 93.7% (95% CI 82.8% to 98.7%) and a specificity of 82.9% (66.3% to 93.4%) to differentiate envenomed patients from those without envenomation. Interrater agreement for identifying hot spots was more than substantial (Kappa statistic >0.85), and intrarater agreement was almost perfect (Kappa = 0.93). Paradoxical thermal changes were observed in 14 patients.ConclusionsPoint-of-care infrared thermal imaging could be useful in the early identification of non-venomous and dry snakebites.Author summaryMost poisonous snakebites cause swelling of the bitten body part within a few hours if venom had been injected. Usually, health care providers diagnose poisonous snakebites by doing a clinical examination and by testing for incoagulable blood. If no abnormalities are found, then the snakebite is diagnosed as a non-poisonous bite or a dry bite. Swelling of the bitten body part results from venom-induced inflammation and is accompanied by local increase in skin temperature. It is possible to capture visual images of these temperature changes by using infrared imaging, the same technology used in night vision cameras. This study found that most persons with poisonous snakebites had hot areas on infrared images while such changes were observed in only a few persons with non-poisonous or dry snakebites. This new knowledge could help doctors identify non-poisonous and dry snakebites early.

scholarly journals GANs-based PIV resolution enhancement without the need of high-resolution input

2021 ◽  
Vol 1 (1) ◽  
Author(s):  
Alejandro Güemes ◽  
Carlos Sanmiguel Vila ◽  
Stefano Discetti
Keyword(s):  
High Resolution ◽  
Resolution Enhancement ◽  
Super Resolution ◽  
Turbulent Channel Flow ◽  
Data Driven ◽  
Generative Adversarial Networks ◽  
Adversarial Networks ◽  
Incomplete Projections ◽  
Data Driven Approach ◽  
High Resolution Images

A data-driven approach to reconstruct high-resolution flow fields is presented. The method is based on exploiting the recent advances of SRGANs (Super-Resolution Generative Adversarial Networks) to enhance the resolution of Particle Image Velocimetry (PIV). The proposed approach exploits the availability of incomplete projections on high-resolution fields using the same set of images processed by standard PIV. Such incomplete projection is made available by sparse particle-based measurements such as super-resolution particle tracking velocimetry. Consequently, in contrast to other works, the method does not need a dual set of low/high-resolution images, and can be applied directly on a single set of raw images for training and estimation. This data-enhanced particle approach is assessed employing two datasets generated from direct numerical simulations: a fluidic pinball and a turbulent channel flow. The results prove that this data-driven method is able to enhance the resolution of PIV measurements even in complex flows without the need of a separate high-resolution experiment for training.

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