Background Thermal Compensation by Filtering for Contrast Enhancement in Active Thermography

Girón, Andrés David Restrepo; Correa, Humberto Loaiza

doi:10.1007/s10921-016-0336-x

Cited by 20 publications

(12 citation statements)

References 13 publications

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Section: Overview Of Noise and Interference Sources In Thermal Images...mentioning

confidence: 99%

“…It is proposed in [9][10][11][12] to jointly solve the problem of filtering impulse interference and increasing the image contrast. In [9] the concept called Background Thermal Compensation by Filtering, BTCF is introduced, in [10] -the method of fast noise suppression. These technologies are implemented by dynamically selecting the size of the filtering window and decimation of the filtered sequence.…”

Section: Overview Of Noise and Interference Sources In Thermal Images...mentioning

confidence: 99%

“…The disadvantages of the methods developed in [9][10][11][12] are typical for traditional median filtering -the complexity of adapting the degree of noise suppression to the level of noise in individual areas of the image and significant computational complexity, which makes it difficult to implement the developed algorithms in real time.…”

Section: Overview Of Noise and Interference Sources In Thermal Images...mentioning

confidence: 99%

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Methods of Compensation of Microbolometer Matriсes Self-Heating in the Processing of Thermal Images

Зуев

Ivashko

Lunin

2022

A.I.S.

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The sources of noise and artifacts arising during thermal imaging and the methods for thermal images filtering, including methods specific for processing of images generated by infrared sensors, are considered. In particular, distortions caused by the process of microbolometrer matrices self-heating due to internal and external heating sources and the methods for compensating such distortions are studied. The purpose of the study is to create a mathematical model of a bolometric matrix self-heating based on heat transfer equations and to develop an algorithm for suppressing of distortions introduced into thermal images by self-heating. The exponential models describing the propagation of heat in the microbolometer matrix are proposed and it is shown that the coefficients of the models after logarithming can be determined by the least squares method. For real thermal images, the coefficients of the model are determined, and situations are considered when the base temperature of the object is known and when it is necessary to restore it, and modifications of the exponential model in the form of an exponent from a complete and incomplete square are proposed. Computer simulation of the proposed distortion compensation algorithm has been carried out, a set of thermal images before and after processing has been presented, and a quantitative estimation of the degree of noise suppression caused by heating of bolometric arrays has been obtained. Based on the results of the work, it was determined that the exponential model provides a sufficient degree of closeness of the experimental and theoretically predicted temperature data, and the degree of difference between the data and the model was estimated. Recommendations are developed for the application of the proposed methods at known and unknown base temperature of the matrix. Proposals have been developed for further improving the mathematical model, including the situation of temperature changes over time, and for improving the efficiency of self-heating noise suppression algorithms.

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Section: Overview Of Noise and Interference Sources In Thermal Images...mentioning

confidence: 99%

Section: Overview Of Noise and Interference Sources In Thermal Images...mentioning

confidence: 99%

Section: Overview Of Noise and Interference Sources In Thermal Images...mentioning

confidence: 99%

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Methods of Compensation of Microbolometer Matriсes Self-Heating in the Processing of Thermal Images

Зуев

Ivashko

Lunin

2022

A.I.S.

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“…The objective of the pulsed thermography experiment is to monitor the surface temperatures of the samples as a function of time and the flow of transient heat generated through an energy stimulus in the samples [3] , [4] , [5] , [6] . The thermal stimulus allows the generation of enough temperature differences to identify sub-surface anomalies if they are present [7] , [8] , [9] .…”

Section: Data Descriptionmentioning

confidence: 99%

Thermal imaging dataset from composite material academic samples inspected by pulsed thermography

et al. 2020

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This paper presents a thermal imaging dataset from composite material samples (carbon and glass fiber reinforced plastic) that were inspected by pulsed thermography with the goal of detecting and characterizing subsurface defective zones (Teflon inserts representing delaminations between plies). The pulsed thermography experiment was applied to 6 academic plates (inspected from both sides) all having the dimensions of 300 mm x 300 mm x 2 mm and same distribution of defects but made of different materials: three plates on carbon fiber-reinforced plastic (CFRP) and three plates made on glass fiber reinforced plastic (GFRP) specimens with three different geometries: planar, curved and trapezoidal. Each plate contains 25 inserts having length/depth ratios between 1.7 and 75. Two FX60 BALCAR photographic flashes (6.2 kJ per flash) were used to generate the heat pulse (2 ms duration), an X6900 FLIR infrared camera using ResearchIR software to record the thermal images and a custom-built software/control unit to synchronize data recording with pulse generation. Finally, the dataset proposed consists of 12 sequences of approximately 2000 images of 512 × 512 pixels each.

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“…In recent years, a number of such methods were developed, which can roughly be divided into three categories. The first category includes thermographic signal reconstruction [2,3], differential absolute contrast [4,5], Gaussian filtering [6], polynomial-based background removal [7,8], mathematical morphology [9], penalized least squares [10], median filtering [11], ensemble empirical mode decomposition [12], etc. These methods are applied to either a single pixel or a single thermogram for noise reduction or/and background elimination.…”

Section: Independent Component Thermography For Non-destructive Testi...mentioning

confidence: 99%

Independent component thermography for non-destructive testing of defects in polymer composites

Liu

et al. 2019

Meas. Sci. Technol.

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Thermographic data processing and analysis is critical for effective infrared thermography non-destructive testing of defects in composite materials. In this research work, the concept of blind source separation is introduced into this field for facilitating subsurface defect detection in polymer composites. In detail, a thermal image generated by infrared thermography is viewed as a linear mixture of several independent and non-Gaussian sources, which can be decomposed by adopting the proposed independent component thermography (ICT) technique. After the decomposition, the non-Gaussian sources are extracted and plotted as a number of images with the same size as the original thermograms. Eventually, the defect signals can be separated from the non-uniform backgrounds which are usually caused by uneven heating. As a result, the location and shape information of the subsurface defective regions is highlighted. The feasibility of the ICT method is illustrated with its application to a carbon fiber reinforced polymer specimen.

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Background Thermal Compensation by Filtering for Contrast Enhancement in Active Thermography

Cited by 20 publications

References 13 publications

Methods of Compensation of Microbolometer Matriсes Self-Heating in the Processing of Thermal Images

Methods of Compensation of Microbolometer Matriсes Self-Heating in the Processing of Thermal Images

Thermal imaging dataset from composite material academic samples inspected by pulsed thermography

Independent component thermography for non-destructive testing of defects in polymer composites

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