Defect Inspection Based on Segmentation and Defective Tracking in Radiographic Image

In this paper, automatic weld defect segmentation into the radiographic image non-destructive evaluation and testing, with orthogonal polynomials transformation-enhancement (OPT-E) is presented. This proposed system defect identification the given defect Radiographic image. In digital radiographic images, the unknown masses appear very light with weak edges, and hence image enhancement technique needs to be applied with transform domain and radiographic images of some illustrative weld deserts invent. The proposed scheme has three phases. In first phase, a radiographic image enhancement technique, which is performed by logarithmic common variance and enhancement factor, computed from the absolute value of the orthogonal polynomials transformation coefficient as principal parameters for increasing the energy of the masses in the digital radiographic image enhancement. In case of successful enhanced of image in addition to gradient estimation scheme is working to point the edges current, in the next phase. The consequential edge image is again applied with orthogonal polynomials. In the final phase, edge tracking are the salient features with angle based defect identification. Experimental is improved quality of images and high relative segmentation by OPT-E.

A novel method of radiographic image enhancement based on phase symmetry

In many important industrial machines, such as nuclear power equipment and steam turbines, the weld quality affects the overall safe operation of the equipment. Although many artificial intelligence (AI)-based technologies are applied to defect recognition using radiographic images, at present the final decision is still made based on human visual inspection. Therefore, it is necessary to enhance the radiographic images to aid the inspection process. In this paper, a method for radiographic testing (RT) weld image enhancement based on phase symmetry, which is based on the principles of human vision, is proposed. Phase symmetry does not depend on the greyscale and contrast information of an image, so it is suitable for RT images with low greyscale values and low contrast. To evaluate the proposed method, 260 RT images acquired by a professional radiographic film digitiser (JD-RDT) and general international RT weld images are used. The results demonstrate that the proposed method can enhance both weld seam and marking information (numbers, letters and symbols), this being suitable for human visual inspection. The proposed method is compared with commonly used methods and the comparison results show that the proposed method achieves an improvement over state-of-the-art methods.

RADIOGRAPHIC IMAGE ENHANCEMENT USING HYBRID ALGORITHM

Radiographic image quality is important in the medical field since it can increase the visibility of anatomical structures and even improve the medical diagnosis. Because the image quality depends on contrast, noise, and spatial resolution, images with low contrast, a lot of noises, or low resolution will decrease image quality, leading to an incorrect diagnosis. Therefore, radiographic images should be enhanced to facilitate medical expertise in making correct diagnosis. In this paper, radiographic images are enhanced by hybrid algorithms based on the idea of combining three image processing techniques: Contrast Limited Adaptive Histogram Equalization for enhancing image contrast, Median Filter for removing noises, and Unsharp Masking for increasing spatial resolution. Two series of medical images consisting of 20 x-ray images and 20 computed radiography images are enhanced with this method. Peak Signal to Noise Ratio (PSNR) and image contrast are computed in order to measure image quality. The results indicate that the enhanced images have better PSNR.

Research of Radiographic Image Enhancement Technology

mage enhancement has applied widely in biomedical, nondestructive testing, satellite remote sensing and other fields. Especially for the low contrast radiographic images, usually there are some disadvantages for a radiographic image such as the local area image does not show a striking contrast. In order to improve the clearness of low contrast radiographic images, in this paper we combined global adaptive equalization with local dynamic enhancement,then we simulate this enhancement algorithm. The new method will not only effectively increase the global contrast of low contrast radiographic images, but also intensify local details. Because the new algorithms contrast enhancement coefficient function can be adjusted dynamically and locally, the new algorithms are not only adaptive to the process of radiographic images but also having great reference value to the other grayscale images.

Digital Radiographic Image Enhancement for Weld Defect Detection using Smoothing and Morphological Transformations

Accurate inspection ofweldedmaterials is important in relation to achieve acceptable standards. Radiography, a non-destructive test method, is commonly used to evaluate the internal condition ofa material with respect to defect detection. Thepresence ofnoise in low resolution ofradiographic images significantly complicates analysis; thereforeattaining higher quality radiographic images makes defect detection more readily achievable. This paper presents a study pertaining to the quality enhancement of radiographic images with respect to different types of defects. A series of digital radiographic weld flaw images were smoothed using multiple smoothing techniques to remove inherent noise followed by top and bottom hat morphological transformations. Image quality was evaluated quantitatively with respect to SNR, PSNR andMAE. The results indicate that smoothing enhances the quality ofradiographic images, thereby promoting defect detection with the respect to original radiographic images.

Digital Radiographic Image Enhancement for Weld Defect Detection using Smoothing and Morphological Transformations

Accurate inspection of welded materials is important in relation to achieve acceptable standards. Radiography, a non-destructive test method, is commonly used to evaluate the internal condition of a material with respect to defect detection. The presence of noise in low resolution of radiographic images significantly complicates analysis; therefore attaining higher quality radiographic images makes defect detection more readily achievable. This paper presents a study pertaining to the quality enhancement of radiographic images with respect to different types of defects. A series of digital radiographic weld flaw images were smoothed using multiple smoothing techniques to remove inherent noise followed by top and bottom hat morphological transformations. Image quality was evaluated quantitatively with respect to SNR, PSNR and MAE. The results indicate that smoothing enhances the quality of radiographic images, thereby promoting defect detection with the respect to original radiographic images.