Improvement of radiography images contrast and ...

Digital Industrial Radiology and Computed Tomography (DIR 2015) 22-25 June 2015, Belgium, Ghent - www.ndt.net/app.DIR2015

DIR2015

International Symposium on Digital Industrial Radiology and Computed Tomography

Improvement of radiography images contrast and detection of the cracks in welds by dehazing algorithm Amir Movafeghi1, Effat Yahaghi1, Nouroddin Mohammadzadeh1 and Berouz Rokrok1 and Nasser Rastkhah1 1

Nuclear Safety and Radiological Protection Department, Nuclear Science and Technology Research Institute, Tehran, Iran. , Email: [email protected] 2 Department of Physics, Imam Khomeini International University, Ghazvin, Iran Email: [email protected]

Abstract Cracks are one of the most important flaws in the welding, which the mechanical strength of materials are significantly determined by existing cracks. Cracks usually appear due to physical stress when the weld cools. Industrial radiography is one of the most important nondestructive testing (NDT) methods which are used for flaw detection in the welds. The correct interpreting of radiographs and its evaluation are depended on the image quality and the interpreter’s experience. In practice, the images often are noisy and insufficient quality. Different methods of image processing can be implemented to increase the image quality and help to reveal the fine flaws. The film radiography method (FR) and the computed radiography method (CR) are two of the most important industrial radiography tools to detect different kind of defects in different structures. Although, because of more sensitivity and greater dynamic range of phosphor imaging plates in the computed radiography than film, CR can produced the clear and sharp images, but the digital radiography images sometimes produce foggy images. This problem also exists in digitized radiography films. The fog or haze is appeared in the radiographic image because of X-ray scattering in material. It causes poor contrast. One of the most common and preventable problems in the radiology is unnecessary fogging of images, which can degrade image quality. In this study, a dehazing algorithm is implemented to the digital radiography images of airplane parts to remove fogs. Dehazing is the process to remove fog or haze effects in images for receiving high performance of the vision algorithm. The results show that the fog removed images have better contrast and the shapes of defects are very clear. In addition, some invisible crack in the digital images can be seen in the fog removed images. The reconstructed radiographic images have improved contrast by dehazing algorithm and the shape and location of cracks appear clearer. Keywords: Welded object; Industrial Radiography; Dehazing Method; Image Processing; Digital Radiography

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1. Introduction Cracks are one of the most important flaws in the welding, which the mechanical strength of materials are significantly determined by existing cracks. Cracks usually appear due to physical stress when the weld cools. Industrial radiography is one of the most important nondestructive testing (NDT) methods which are used for flaw detection in the welds. The correct interpreting of radiographs and its evaluation are depended on the image quality and the interpreter’s experience. The quality of radiograph in welded objects is easily undermined by scattered X-ray in material. In practice, the images often are noisy and insufficient quality. This has an effect on the image, e.g., contrasts are reduced and the edge is blurred. One of the most common problems in radiology is fogged images; however, the fog can be eliminated by cancelling out the sources of fog, such as incorrect safelight filters, light leaks, excessive safelight wattage, and indicator lights on telephones, or other electrical devices [1-2]. However, it is not possible to completely remove all sources of fog. Therefore, use of the dehazing method of image processing can be useful to improve the contrast of image and the remove fog [4-5]. The goal of haze removal algorithms is to enhance and recover the detail of the scene from haze image.

2. Methods

2.1 Digital Radiography Images

Radiographic experiments were executed on the welded objects using a 300 kVolt x-ray machine (Pantak-Seifert, Type: Eresco 65 MF2). This X-ray system was used for all radiographic exposures. Industrial radiographic films, such as KODAK MX-125 and AA400 films, were used for necessary radiographic tests [3-5].

2.2 Dehazing method Fog removal or dehazing method is the process of removing fog or haze effects in the images and receiving high performance of the vision algorithm. In the He model, every local patch in the enhanced image should have at least one colour component near zero [6]. In the other words, the work assumed most scenes are made up of either dark or colourful objects. The transmission of each patch was estimated as the minimum colour component within that patch. It is assumed that for a scene point P, I is the observed color vector on a foggy or hazy

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෡ represents the direction of the direct transmission color of P and the day. The unit vector ‫ܦ‬

unit vector ‫ܣ‬መ represent the direction of the air-light color. Then, it can be written [7-8]: ෡ ൅ ‫ܣݍ‬መ ‫ ܫ‬ൌ ‫ܦ݌‬

where p=R eíȕd (R=݃

ூబ ሺఒሻ ௗమ

(1)

) is the magnitude of direct transmission, and q = E(1 í eíȕd) is

the magnitude of air-light (fog or haze) of P. We can express equation (1) as follows:

I(d)= t(d)J(d)+(1-t(d))A

(2)

where I(d) is the observed image, t(d)= eíȕd, J(x) is sense radiation, and A is global atmospheric light. By estimating t(d) and A , the scene radiance can be obtained by: ‫ܬ‬ሺ݀ሻ ൌ

‫ܫ‬ሺ݀ሻ െ ‫ܣ‬ ൅‫ܣ‬ ‫ݐ‬ሺ݀ሻ

(3)

A can be simply computed by averaging a patch of the sky on a foggy or hazy day and the estimated t(d) expressed by different models based on boundary constrain [9,10].

3. Results

The algorithm is applied to the digital radiography images that have crack defects. A typical example of the main and the fog removal images are shown in figure. 1(a) and (b), respectively. Comparison between the main and the fog removed image shows that the fog removed images by the dehazing algorithm are significantly clearer; such defects as crack region are also clear. Also, the depths and shapes of the cracks are visible in Fig. 1(b). The results show that the fog removal method is more suitable for detection of crack defects and the crack regions are improved contrast. Also, the shape and location of cracks appear clearer. In Fig. 2, the line profile of the chosen region in Fig. 1-a,b (the lines AB and A1B1 in original image and the dehazed image, respectively) are bolted and shows that the pixel intensity in crack region is better in the dehazed image.

DIR2015

Internatioonal Symposiu um on Digital Industrial Raadiology and Computed C Tom mography

a

b

Figure 1 A typical example e of a) the mainn and b) the fog removaal images

Fig. 22: the line prrofile of thee chosen reggion in Fig. 1-a,b (the liines AB and d A1B1 in original image an nd the dehazzed image

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4. Conclusion In the research, the dehazing method is implemented to remove fog from the radiograph welded objects. The welded objects have the crack defect. The Crack is one of the major flaws in the welding. Cracks usually appear due to physical stress when the weld cools. The correct interpreting of radiographs and its evaluation are depended to the image quality and the interpreter’s experience. In practice, the images often are very noisy and low quality. Therefore the dehazing method can improve the contrast of the radiograph. A fog removal algorithm is applied to the radiography images of welded objects. The methods do not require precise information about the scene or the X-ray condition. The method is beneficial for generic image dehazing; it often works for particular applications

References

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