scholarly journals Thermal Image Restoration Based on LWIR Sensor Statistics

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5443
Author(s):  
Jaeduk Han ◽  
Haegeun Lee ◽  
Moon Gi Kang
Keyword(s):  
Thermal Imaging ◽  
Imaging System ◽  
Similarity Index ◽  
Optimization Method ◽  
Thermal Image ◽  
Reference Image ◽  
Thermal Images ◽  
Effective Roughness ◽  
Leibler Divergence ◽  
Natural Statistics

An imaging system has natural statistics that reflect its intrinsic characteristics. For example, the gradient histogram of a visible light image generally obeys a heavy-tailed distribution, and its restoration considers natural statistics. Thermal imaging cameras detect infrared radiation, and their signal processors are specialized according to the optical and sensor systems. Thermal images, also known as long wavelength infrared (LWIR) images, suffer from distinct degradations of LWIR sensors and residual nonuniformity (RNU). However, despite the existence of various studies on the statistics of thermal images, thermal image processing has seldom attempted to incorporate natural statistics. In this study, natural statistics of thermal imaging sensors are derived, and an optimization method for restoring thermal images is proposed. To verify our hypothesis about the thermal images, high-frequency components of thermal images from various datasets are analyzed with various measures (correlation coefficient, histogram intersection, chi-squared test, Bhattacharyya distance, and Kullback–Leibler divergence), and generalized properties are derived. Furthermore, cost functions accommodating the validated natural statistics are designed and minimized by a pixel-wise optimization method. The proposed algorithm has a specialized structure for thermal images and outperforms the conventional methods. Several image quality assessments are employed for quantitatively demonstrating the performance of the proposed method. Experiments with synthesized images and real-world images are conducted, and the results are quantified by reference image assessments (peak signal-to-noise ratio and structural similarity index measure) and no-reference image assessments (Roughness (Ro) and Effective Roughness (ERo) indices).

Innovative yield mapping system using hyperspectral and thermal imaging for precision tree crop management

2014 ◽  
Author(s):  
W.S. Lee ◽  
Victor Alchanatis ◽  
Asher Levi
Keyword(s):  
Image Registration ◽  
Navigation System ◽  
Thermal Imaging ◽  
Imaging System ◽  
Citrus Fruit ◽  
Mobile Platform ◽  
Yield Estimation ◽  
Yield Mapping ◽  
Thermal Images ◽  
Mapping System

Original objectives and revisions – The original overall objective was to develop, test and validate a prototype yield mapping system for unit area to increase yield and profit for tree crops. Specific objectives were: (1) to develop a yield mapping system for a static situation, using hyperspectral and thermal imaging independently, (2) to integrate hyperspectral and thermal imaging for improved yield estimation by combining thermal images with hyperspectral images to improve fruit detection, and (3) to expand the system to a mobile platform for a stop-measure- and-go situation. There were no major revisions in the overall objective, however, several revisions were made on the specific objectives. The revised specific objectives were: (1) to develop a yield mapping system for a static situation, using color and thermal imaging independently, (2) to integrate color and thermal imaging for improved yield estimation by combining thermal images with color images to improve fruit detection, and (3) to expand the system to an autonomous mobile platform for a continuous-measure situation. Background, major conclusions, solutions and achievements -- Yield mapping is considered as an initial step for applying precision agriculture technologies. Although many yield mapping systems have been developed for agronomic crops, it remains a difficult task for mapping yield of tree crops. In this project, an autonomous immature fruit yield mapping system was developed. The system could detect and count the number of fruit at early growth stages of citrus fruit so that farmers could apply site-specific management based on the maps. There were two sub-systems, a navigation system and an imaging system. Robot Operating System (ROS) was the backbone for developing the navigation system using an unmanned ground vehicle (UGV). An inertial measurement unit (IMU), wheel encoders and a GPS were integrated using an extended Kalman filter to provide reliable and accurate localization information. A LiDAR was added to support simultaneous localization and mapping (SLAM) algorithms. The color camera on a Microsoft Kinect was used to detect citrus trees and a new machine vision algorithm was developed to enable autonomous navigations in the citrus grove. A multimodal imaging system, which consisted of two color cameras and a thermal camera, was carried by the vehicle for video acquisitions. A novel image registration method was developed for combining color and thermal images and matching fruit in both images which achieved pixel-level accuracy. A new Color- Thermal Combined Probability (CTCP) algorithm was created to effectively fuse information from the color and thermal images to classify potential image regions into fruit and non-fruit classes. Algorithms were also developed to integrate image registration, information fusion and fruit classification and detection into a single step for real-time processing. The imaging system achieved a precision rate of 95.5% and a recall rate of 90.4% on immature green citrus fruit detection which was a great improvement compared to previous studies. Implications – The development of the immature green fruit yield mapping system will help farmers make early decisions for planning operations and marketing so high yield and profit can be achieved. 

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.

Quantitative Evaluation of Skin Lesions Using Transient Thermal Imaging

2010 ◽  
Author(s):  
Mu¨ge Pirtini C¸etingu¨l ◽  
Rhoda M. Alani ◽  
Cila Herman
Keyword(s):  
Thermal Imaging ◽  
Imaging System ◽  
Skin Lesions ◽  
Malignant Potential ◽  
Clinical Indication ◽  
Scanning System ◽  
Proliferative Potential ◽  
Thermal Images ◽  
Pigmented Lesions ◽  
Transient Thermal

We have recently developed a thermal (infrared - IR) imaging system that allows accurate measurements of transient temperature distributions of the skin surface. It relies on active infrared imaging for the characterization of skin lesions. We hypothesize that lesions with increased proliferative potential or inflammatory processes in the skin generate quantifiable amounts of heat and possess an ability to reheat more quickly than the surrounding normal skin. We demonstrate that the visualization and measurement of the transient thermal response of the skin to a cooling excitation can aid the identification of skin lesions of different origin. In the preliminary studies we focused on optimizing this high-resolution infrared scanning system in order to evaluate thermal images of the skin with the aim to distinguish benign from malignant pigmented lesions. Such an imaging tool is expected to improve the diagnostic accuracy and sensitivity for melanoma as well as other skin cancers, while decreasing the number of unnecessary biopsies. Therefore, we are currently conducting a pilot clinical trial of our transient thermal imaging system to verify the feasibility of the described approach in distinguishing pigmented lesions of varying malignant potential. In our trial patients who possess a pigmented lesion with a clinical indication for biopsy are selected to participate in the study. After scanning the lesions, they are biopsied and graded for malignant potential. Biopsy results are correlated with thermal images and bright light images in order to evaluate thermal associations with malignant potential.

scholarly journals Design of a Thermal Imaging System to Detect Possible Cases of COVID-19 Patients

2021 ◽  
Vol 11 (9) ◽  
pp. 66-72
Author(s):  
Luis Nuñez Tapia ◽  
◽  
Brian Meneses Claudio ◽  
Witman Alvarado Díaz
Keyword(s):  
Body Temperature ◽  
Thermal Imaging ◽  
Imaging System ◽  
National Level ◽  
Processing System ◽  
Thermal Image ◽  
The Body ◽  
Thermal Camera ◽  
The Government ◽  
Thermal Imaging System

Abstract— The problem that the world is currently facing and that has claimed more than 3.2 million lives worldwide is COVID-19. Being a highly contagious disease, the WHO recommended limiting the movement of people out of their residence. Given this, Peru took the necessary measures to control the spread of this virus, therefore, the government decreed a general quarantine in the country, which from March 6th to May 9th, 2020, had reported the death of 1814 people in the country, because the health system at the national level was not prepared for such magnitude, in addition to that the number of infections continued to grow since they do not respect social distancing. In view of this problem, this article will design a thermal image processing system to detect possible cases of patients with COVID-19, in such a way that it allows companies or institutions to know the body temperature of each person, and thus know they are possibly COVID-19 patients. Through the design of this system, it will be possible to measure body temperature with the drone and the thermal camera at 50 cm from the person, in such a way that if the person has a body temperature higher than 38°C it could be infected with COVID-19. Therefore, the implementation of this system will help reduce the number of infections within an institution or workplace. Keywords-- Thermal camera, COVID-19, Drone, MATLAB, Image.

scholarly journals A Review on Surveillance using Thermal Imaging System for Animals and Birds in the Field of Agriculture

2020 ◽  
pp. 340-345
Author(s):  
Dipti Anurag Doshi
Keyword(s):  
Thermal Imaging ◽  
Imaging System ◽  
Imaging Techniques ◽  
Crop Protection ◽  
Thermal Image ◽  
Heat Leakage ◽  
Crop Disease ◽  
Thermal Imaging System ◽  
The Military ◽  
Border Surveillance

- Thermal imaging systems are most commonly associated with military and defense. It is most used by the military, army and in navy also for border surveillance and law enforcement. Also in the detection of heat leakage from buildings, inspection of defects on technical installations or building protection against the unwanted entry of undesirable persons. In the field of healthcare, both for humans and animals also it used. By considering the various application of thermal image this technique is helpful for farmers in detection of crop diseases. Not only crop disease but also in the detection of animals. This paper intends to focus on the survey on application of thermal image processing in agriculture field such as imaging techniques for crop protection in order to monitor the birds and animals.

scholarly journals Deep Learning Based Object Recognition in Real Time Images Using Thermal Imaging System

2021 ◽  
Author(s):  
Rohini Goel ◽  
Avinash Sharma ◽  
Rajiv Kapoor
Keyword(s):  
Object Recognition ◽  
Real Time ◽  
Thermal Imaging ◽  
Imaging System ◽  
Night Vision ◽  
Driver Assistance ◽  
Thermal Images ◽  
Deep Network ◽  
The One ◽  
Thermal Imaging System

An efficient driver assistance system is essential to avoid mishaps. The collision between the vehicles and objects before vehicle is the one of the principle reason of mishaps that outcomes in terms of diminished safety and higher monetary loss. Researchers are interminably attempting to upgrade the safety means for diminishing the mishap rates. This paper proposes an accurate and proficient technique for identifying objects in front of vehicles utilizing thermal imaging framework. For this purpose, image dataset is obtained with the help of a night vision IR camera. This strategy presents deep network based procedure for recognition of objects in thermal images. The deep network gives the model understanding of real world objects and empowers the object recognition. The real time thermal image database is utilized for the training and validation of deep network. In this work, Faster R-CNN is used to adequately identify objects in real time thermal images. This work can be an incredible help for driver assistance framework. The outcomes exhibits that the proposed work assists to boost public safety with good accuracy.

scholarly journals Microwave Imaging of Breast Skin Utilizing Elliptical UWB Antenna and Reverse Problems Algorithm

Micromachines ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 647
Author(s):  
Sameer Alani ◽  
Zahriladha Zakaria ◽  
Tale Saeidi ◽  
Asmala Ahmad ◽  
Muhammad Ali Imran ◽  
...  
Keyword(s):  
Skin Cancer ◽  
Imaging System ◽  
Signal Transmission ◽  
Similarity Index ◽  
Structural Similarity ◽  
Microwave Imaging ◽  
Ultra Wideband ◽  
Uwb Antenna ◽  
System A ◽  
High Structural Similarity

Skin cancer is one of the most widespread and fast growing of all kinds of cancer since it affects the human body easily due to exposure to the Sun’s rays. Microwave imaging has shown better outcomes with higher resolution, faster processing time, mobility, and less cutter and artifact effects. A miniaturized elliptical ultra-wideband (UWB) antenna and its semi-spherical array arrangement were used for signal transmission and reception from the defected locations in the breast skin. Several conditions such as various arrays of three, six, and nine antenna elements, smaller tumor, multi-tumors, and skin on a larger breast sample of 30 cm were considered. To assess the ability of the system, a breast shape container with a diameter of 130 mm and height of 60 mm was 3D printed and then filled with fabricated skin and breast fat to perform the experimental investigation. An improved modified time-reversal algorithm (IMTR) was used to recreate 2D images of tumors with the smallest radius of 1.75 mm in any location within the breast skin. The reconstructed images using both simulated and experimental data verified that the system can be a reliable imaging system for skin cancer diagnosis having a high structural similarity index and resolution.

scholarly journals Activity Recognition in Residential Spaces with Internet of Things Devices and Thermal Imaging

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 988
Author(s):  
Kshirasagar Naik ◽  
Tejas Pandit ◽  
Nitin Naik ◽  
Parth Shah
Keyword(s):  
Internet Of Things ◽  
Activity Recognition ◽  
Thermal Model ◽  
Thermal Image ◽  
Heat Radiation ◽  
Household Activity ◽  
Thermal Images ◽  
Gas Leakage ◽  
3D Space ◽  
Image Pairs

In this paper, we design algorithms for indoor activity recognition and 3D thermal model generation using thermal images, RGB images, captured from external sensors, and the internet of things setup. Indoor activity recognition deals with two sub-problems: Human activity and household activity recognition. Household activity recognition includes the recognition of electrical appliances and their heat radiation with the help of thermal images. A FLIR ONE PRO camera is used to capture RGB-thermal image pairs for a scene. Duration and pattern of activities are also determined using an iterative algorithm, to explore kitchen safety situations. For more accurate monitoring of hazardous events such as stove gas leakage, a 3D reconstruction approach is proposed to determine the temperature of all points in the 3D space of a scene. The 3D thermal model is obtained using the stereo RGB and thermal images for a particular scene. Accurate results are observed for activity detection, and a significant improvement in the temperature estimation is recorded in the 3D thermal model compared to the 2D thermal image. Results from this research can find applications in home automation, heat automation in smart homes, and energy management in residential spaces.

Fused Image Quality Measure Based on Structural Similarity

2011 ◽  
Vol 255-260 ◽  
pp. 2072-2076
Author(s):  
Yi Yong Han ◽  
Jun Ju Zhang ◽  
Ben Kang Chang ◽  
Yi Hui Yuan ◽  
Hui Xu
Keyword(s):  
Image Fusion ◽  
Structural Information ◽  
Similarity Index ◽  
Structural Similarity ◽  
Reference Image ◽  
Human Visual Perception ◽  
Subjective Evaluations ◽  
Fused Image ◽  
Fusion Methods ◽  
Image Quality Measure

Under the assumption that human visual perception is highly adapted for extracting structural information from a scene, we present a new approach using structural similarity index for assessing quality in image fusion. The advantages of our measures are that they do not require a reference image and can be easily computed. Numerous simulations demonstrate that our measures are conform to subjective evaluations and can be able to assess different image fusion methods.

scholarly journals Synthetic polarization-sensitive optical coherence tomography by deep learning

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Yi Sun ◽  
Jianfeng Wang ◽  
Jindou Shi ◽  
Stephen A. Boppart
Keyword(s):  
Optical Coherence Tomography ◽  
Deep Learning ◽  
Imaging System ◽  
Imaging Modality ◽  
Similarity Index ◽  
Polarization Sensitivity ◽  
Imaging Method ◽  
Optical Coherence ◽  
The Real ◽  
Oct Imaging

AbstractPolarization-sensitive optical coherence tomography (PS-OCT) is a high-resolution label-free optical biomedical imaging modality that is sensitive to the microstructural architecture in tissue that gives rise to form birefringence, such as collagen or muscle fibers. To enable polarization sensitivity in an OCT system, however, requires additional hardware and complexity. We developed a deep-learning method to synthesize PS-OCT images by training a generative adversarial network (GAN) on OCT intensity and PS-OCT images. The synthesis accuracy was first evaluated by the structural similarity index (SSIM) between the synthetic and real PS-OCT images. Furthermore, the effectiveness of the computational PS-OCT images was validated by separately training two image classifiers using the real and synthetic PS-OCT images for cancer/normal classification. The similar classification results of the two trained classifiers demonstrate that the predicted PS-OCT images can be potentially used interchangeably in cancer diagnosis applications. In addition, we applied the trained GAN models on OCT images collected from a separate OCT imaging system, and the synthetic PS-OCT images correlate well with the real PS-OCT image collected from the same sample sites using the PS-OCT imaging system. This computational PS-OCT imaging method has the potential to reduce the cost, complexity, and need for hardware-based PS-OCT imaging systems.

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