An Image Enhancement Algorithm Based on Histogram Nonlinear Transform

Zhi-guo, 武治国 Wu; Yan-jie, 王延杰 Wang

doi:10.3788/gzxb20103904.0755

Cited by 9 publications

(1 citation statement)

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“…Base on the analysis of the images, in this paper, we used the subsection linear stretching histogram equalization to enhance the contrast of these images in order to apply edge detection to produce outline of molten pool and its shadow. The goal of histogram equalization is transforming histogram of these images to be uniform distribution, yet sometimes the distributions are not like that totally [3]. In our method, we first divided the dynamic range of histogram of the infrared images into M parts, and in every part the dynamic range is not uniform, but every sub-region (Marked as Tm) should be satisfied with the following equation:…”

Section: Image Acquisition and Analysismentioning

confidence: 99%

Segmentation of Molten Pool Infrared Images Using Morphology during High Power Disk Laser Welding

Zhang

Gao

2012

AMM

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Configuration of a molten pool is related to the laser welding quality. Analyzing the configuration of a molten pool is important to monitor the laser welding process. This paper proposes a method of segmentation of a molten pool and its shadow during high power disk laser welding, consequently provides the groundwork for reconstruction of the molten pool and analysis of welding quality. Subsection linear stretching histogram equalization was applied to enhance the contrast of the original images firstly, and then edge detection was used to highlight the edges. After that we used the morphology filtering method to produce the segmentation mask, and then combined the mask with the original images to get the final segmentation results. Also, the proposed method was compared with other traditional methods. The experimental results showed that our method not only could give better segmentation results and process large quantities images automatically, but also overcame the less-segmentation problems of traditional methods.

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Section: Image Acquisition and Analysismentioning

confidence: 99%