Noise Reduction in Thermal Images

Budzan, Sebastian; Wyżgolik, R.

doi:10.1007/978-3-319-11331-9_15

Cited by 11 publications

(8 citation statements)

References 14 publications

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“…Budzan and Wyżgolik [ 51 ] indicated that the noise sources of TIR images are mainly from TIR sensors and the interference of the signal processing circuit. The non-uniformity of focal plane array (FPA) uncooled microbolometers results from different response (gain and offset) of the FPA pixels due to the fabrication process.…”

Section: Methodsmentioning

confidence: 99%

Assessment of Cracking Widths in a Concrete Wall Based on TIR Radiances of Cracking

2020

Sensors

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The techniques of concrete crack detection, as well as assessments based on thermography coupled with ultrasound, have been presented in many works; however, they have generally needed an additional source of thermal infrared (TIR) radiance and have only been applied in laboratories. Considering the accessibility of thermal infrared cameras, a TIR camera (NEC F30W) was employed to detect cracking in the concrete wall of an historic house with a western architectural style in Kinmen, Taiwan, based on the TIR radiances of cracking. An operation procedure involving a series of image processing and statistical analysis processes was designed to evaluate the performance of the TIR camera in the assessment of the cracking width. This procedure using multiple measurements was implemented from March to August 2019, and the t-tests indicated that the temperature differences between the inside and outline of the concrete cracks remained insignificant as the temperature or relative humidity (RH) in the subtropical climate rose. The experimental results of the operation procedure indicated that the maximum focusing range, which is related to the size of the sensor array, and the minimum detectable crack width of a TIR camera should be 1.0 m and 6.0 mm, respectively, in order to derive a linear regression model with a determination coefficient R2 of 0.733 to estimate the cracking widths, based on the temperature gradients. The validation results showed that there was an approximate R2 value of 0.8 and a total root mean square error of ±2.5 mm between the cracking width estimations and the observations.

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Section: Methodsmentioning

confidence: 99%

Assessment of Cracking Widths in a Concrete Wall Based on TIR Radiances of Cracking

2020

Sensors

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“…Thermographic images present typical stochastic noise [34], due to temperature fluctuation noise in thermal sensors coupled with some influence of the manufacturing process [35] and the emissivity fluctuations of observed objects and background. All these factors contribute to a non-uniformity in the response of the thermal sensor, leading to various responses of the pixels.…”

Section: Image Processing Pipelinementioning

confidence: 99%

Evaluation of 3D/2D Imaging and Image Processing Techniques for the Monitoring of Seed Imbibition

et al. 2018

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Seed imbibition is a very important process in plant biology by which, thanks to a simple water income, a dry seed may turn into a developing organism. In natural conditions, this process occurs in the soil, e.g., with difficult access for a direct observation. Monitoring the seed imbibition with non-invasive imaging techniques is therefore an important and possibly challenging task if one tries to perform it in natural conditions. In this report, we describe a set of four different imaging techniques that enable to addressing this task either in 3D or in 2D. For each technique, the following items are proposed. A detailed experimental protocol is provided to acquire images of the imbibition process. With the illustration of real data, the significance of the physical quantities measured in terms of their relation to the income of water in the seed is presented. Complete image analysis pipelines are then proposed to extract dynamic information on the imbibition process from such monitoring experiments. A final discussion compares the advantages and current limitations of each technique in addition to elements concerning the associated throughput and cost. These are criteria especially relevant in the field of plant phenotyping where large populations of plants are imaged to produce quantitatively significative traits after image processing.

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“…Also the environmental conditions during the measurement should be considered. The important problem is thermal stabilization of the TIR camera as stated by Budzan and Wyżgolik (2014). The time required to stabilize the thermal properties of an TIR camera differ according to the camera manufacturer.…”

Section: Related Workmentioning

confidence: 99%

“…Image histogram and percentile exampleBudzan and Wyżgolik (2014) state that the main source of error in TIR images is the non-uniformity of the focal plane array (FPA) and read-out circuits of TIR cameras. The aforementioned problems cause TIR detector non-uniformity, what is revealed by varied response of the pixels.…”

mentioning

confidence: 99%

Thermal Ir Imaging: Image Quality and Orthophoto Generation

Sledz

Unger

Heipke

2018

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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<p><strong>Abstract.</strong> This paper deals with two aspects of photogrammetric processing of thermal images: image quality and 3D reconstruction quality. The first aspect of the paper relates to the influence of day light on Thermal InfraRed (TIR) images captured by an Unmanned Aerial Vehicle (UAV). Environmental factors such as ambient temperature and lack of sun light affect TIR image quality. We acquire image sequences of the same object during day and night and compare the generated orthophotos according to different metrics like contrast and signal-to-noise ratio (SNR). Our experiments show that performing TIR image acquisition during night time provides a better thermal contrast, regardless of whether we compute contrast over the whole image or over small patches. The second aspect investigated in this work is the potential of using TIR images for photogrammetric tasks such as the automatic generation of Digital Surface Models (DSM) and orthophotos. Due to the low geometrical resolution of a TIR camera and the low image quality in terms of contrast and noise compared to RGB images, the TIR DSM suffers from reconstruction errors and an orthophoto generated using the TIR DSM and TIR images is visibly influenced by those errors. We therefore include measurements of the UAVs positions during image capturing provided by a Global Navigation Satellite System (GNSS) receiver to retrieve position and orientation of TIR and RGB images in the same world coordinate system. To generate an orthophoto from TIR images, they are projected onto the DSM reconstructed from RGB images. This procedure leads to a TIR orthophoto of much higher quality in terms of geometrical correctness.</p>

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Noise Reduction in Thermal Images

Cited by 11 publications

References 14 publications

Assessment of Cracking Widths in a Concrete Wall Based on TIR Radiances of Cracking

Assessment of Cracking Widths in a Concrete Wall Based on TIR Radiances of Cracking

Evaluation of 3D/2D Imaging and Image Processing Techniques for the Monitoring of Seed Imbibition

Thermal Ir Imaging: Image Quality and Orthophoto Generation

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