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A Display Enhancement Method for High-bit RAW Images on Low-bit Monitor
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  • Zhigang Lv,
  • Liangliang Li,
  • Hongxi Wang,
  • Peng Wang,
  • Jianheng Li,
  • Lei Shu,
  • Xiaoyan Li
Zhigang Lv
Xian Technological University

Corresponding Author:[email protected]

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Liangliang Li
Xian Technological University
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Hongxi Wang
Xian Technological University
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Peng Wang
Xian Technological University
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Jianheng Li
Xian Technological University
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Lei Shu
Xian Technological University
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Xiaoyan Li
Xian Technological University
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Abstract

X-ray flaw detection is widely used in the non-destructive testing method for industrial pressure vessels and the intuitive defect information can be obtained through the X-ray film. Digitizing X-ray film and realizing online evaluation of weld information is a focus in this field. To meet the requirements of DS level in the EN1409 international standard, a 12-bit or 16-bit industrial camera must be used to digitize the X-ray film, to ensure that the obtained high-gray-scale image has more details and more obvious features. Due to the low resolution of the grayscale image by the human eye, there is a relatively large error when evaluating the weld seam information in the grayscale image with low overall brightness and contrast. In addition, when an ordinary 8-bit monitor displays the above grayscale image with a high bit depth, problems, such as loss of detail information and blurring of the image, will occur due to the mismatch of the bit depth, resulting in greater errors. Aiming at the above problems, this paper proposed an image enhancement algorithm for displaying high-bit RAW images on the low-bit monitor. The pseudo-color enhancement algorithm was used in high gray-scale weld images, and a reasonable chromatographic mapping relationship was formulated according to the visual characteristics of the human eye to solve the problem of information loss caused by bit depth mismatch. Through pseudo-color processing, the defect information and the background color of the weld showed different colors, which was convenient for the capture of defect information in the weld. Compared with the existing algorithms, various indicators have been greatly improved, enriching the amount of information and strengthening the image recognition effects.