Non-destructive testing (NDT) is a broad group of testing and analysis techniques used in science and industry to evaluate the properties of a material, structure, or system for characteristic defects and discontinuities without causing damage. Recently, infrared thermography is one of the most promising technologies as it can inspect a large area quickly using a non-contact and non-destructive method. Moreover, thermography testing has proved to be a valuable approach for non-destructive testing and evaluation of structural stability of materials. Pulsed thermography is one of the active thermography technologies that utilizes external energy heating. However, due to the non-uniform heating, lateral heat diffusion, environmental noise, and limited parameters of the thermal imaging system, there are some difficulties in detecting and characterizing defects. In order to improve this limitation, various signal processing techniques have been developed through many previous studies. This review presents the latest advances and exhaustive summary of representative signal processing techniques used in pulsed thermography according to physical principles and thermal excitation sources. First, the basic concept of infrared thermography non-destructive testing is introduced. Next, the principle of conventional pulsed thermography and signal processing technologies for non-destructive testing are reviewed. Then, we review advances and recent advances in each signal processing. Finally, the latest research trends are reviewed.

This study performed an experimental investigation on pulsed thermography to detect internal defects, the major degradation phenomena in several structures of the secondary systems in nuclear power plants as well as industrial pipelines. The material losses due to wall thinning were simulated by drilling flat-bottomed holes (FBH) on the steel plate. FBH of different sizes in varying depths were considered to evaluate the detection capability of the proposed technique. A short and high energy light pulse was deposited on a sample surface, and an infrared camera was used to analyze the effect of the applied heat flux. The three most established signal processing techniques of thermography, namely thermal signal reconstruction (TSR), pulsed phase thermography (PPT), and principal component thermography (PCT), have been applied to raw thermal images. Then, the performance of each technique was evaluated concerning enhanced defect detectability and signal to noise ratio (SNR). The results revealed that TSR enhanced the defect detectability, detecting the maximum number of defects, PPT provided the highest SNR, especially for the deeper defects, and PCT provided the highest SNR for the shallower defects.

Active infrared thermography is an attractive and reliable technique used for the non-destructive evaluation of various materials and structures, because it enables non-contact, large area, high-speed, quantitative, and qualitative inspection. However, the defect detectability is significantly deteriorated due to the excitation of a non-uniform heat source and surrounding environmental noise, requiring additional signal processing and image characterization. The lock-in infrared thermography technique has been proven to be an effective method for quantitative evaluation by extracting amplitude and phase images from a 2D thermal sequence, but it still involves a lot of noise, providing difficulties in detection. Therefore, this study explored the possibility of improving the signal-to-noise ratio by applying filtering to a stainless-steel plate with circular defects. Thereafter, automated defect detection was performed based on the threshold value through the binary images. In addition, a comparative analysis was performed to evaluate the detectability according to the presence or absence of a filtering application.

An aberration correction method is introduced for 3D phase deconvolution microscopy. Our technique capitalizes on multiple illumination patterns to iteratively extract Fourier space aberrations, utilizing the overlapping information inherent in these patterns. By refining the point spread function based on the retrieved aberration data, we significantly improve the precision of refractive index deconvolution. We validate the effectiveness of our method on both synthetic and biological three-dimensional samples, achieving notable enhancements in resolution and measurement accuracy. The method's reliability in aberration retrieval is further confirmed through controlled experiments with intentionally induced spherical aberrations, underscoring its potential for wide-ranging applications in microscopy and biomedicine.

Active infrared thermography is an attractive and highly reliable technique used for the non-destructive evaluation of test objects. In this paper, defect detection on the subsurface of the STS304 metal specimen was performed by applying the line-scanning method to induction thermography. In general, the infrared camera and the specimen are fixed in induction thermography, but the line-scanning method can excite a uniform heat source because relative movement occurs. After that, the local heating area due to Joule’s heating effect was removed, and filtering was applied for the 1st de-noising. Threshold-value-based binarization processing using the Otsu algorithm was performed for clear defect object recognition. After performing the 2nd de-noising, automatic defect recognition was performed using a boundary tracking algorithm. As a result, the conditions due to the parameters of the scanning line for the thermal image were determined.