## 1 Introduction  
Defects in components can severely reduce the strength of a structure and may even result in a serious failure of the structure. Non-destructive testing (NDT) is an essential technique to detect defects on surface and subsurface defects in components. The objectives of NDT include detection of defects and their characterization. Detection of defects aims to judge whether defects are present in the tested specimen or not [1; 2]. Characterization of defects is to determine dimensions of defects, including depth, thickness, and size. The size of a defect is a critical factor which should be considered when evaluating the remaining useful life and the capacity of a metallic component. Decision-making in maintenance management of a metallic component usually depends on the size information of the detected defects, and many NDT techniques have been used for defect sizing [3; 4]. Consequently, accurate sizing of defects is of great importance in NDT of metallic components. Infrared (IR) thermography, an important NDT technique, has been widely applied to test metallic components due to its advantages in terms of non-contact, efficiency, and safety [5; 6; 7; 8; 9]. The effectiveness of IR thermography in detecting subsurface defect in metallic components has widely been validated in many studies [10; 11; 12]. However, defects usually present weak indications in thermograms. Thermograms have more random noise compared to natural images due to the intrinsic principle of IR thermography [13]. Moreover, surfaces of test objects are not always ideal for processing with black matt painting, which is a common way to enhance defect indications in IR thermography. For example, for a metallic part with a corroded surface, defect indications in thermograms are blurrier due to the effect of corrosion material. Non-uniform heating also makes some defects blurry in thermograms. Under non-uniform heating condition, defects obtaining less heating energy present weaker indications than those in regions with intensive irradiation. Sizing of these blurry defects with a high accuracy is significantly difficult in IR thermography. Much research has focused on sizing defect in IR thermography. In general, these approaches can be classified into temperature profile-based, point-spread function (PSF)-based, and image processing-based approaches. Full width at half maximum (FWHM) is the most widely used temperature profile-based approach [14; 15]. In FWHM, the width of half maximum gray level along horizontal and vertical lines in a selected IR thermal image is employed to describe the size of a defect. [PERSON] et al. thus proposed a gradient-based sizing means for pulsed IR thermography, which is still widely being used today [16; 17; 18]. [PERSON] proposed a sizing method by computing the product of the derivative of contrast and contrast gradient in [13]. All of these approaches can size distinct defects with satisfactory accuracies. However, for blurry defects that present low observable indications, the above-mentioned approaches usually cannot perform with a sufficient accuracy. Moreover, difficulties in searching for the peak temperature location and in selecting the reference temperature make sizing blurry defects a time-consuming work. PSF-based sizing approaches regard a captured thermal image as the convolution of the defect shape function and a PSF. With a known PSF, the contour of a defect can be retrieved by implementing deconvolution on the captured thermal image. Several PSF models have been proposed in IR thermography [19; 20]. However, PSF is a function of defect depth. The contour of a defect can only be retrieved accurately on the condition that the depth of the defect is known. In other words, depth retrievement should be implemented before sizing a defect when using PSF-based approaches. This introduced step increases the workload of inspectors significantly. Image processing is another means of sizing defects in IR thermography.