Research On Industrial Ray Image Enhancement Algorithm

With the development of testing technology,X-ray non-destructive testing has now become an important testing technology,which has the advantages of strong penetration and rich image information,X-ray non-destructive testing has many applications in both industrial devices and medical equipment.However,due to the diverse structure of the detection workpiece,different materials,and the defects of the imaging equipment itself,the X-ray image has the disadvantages of low contrast and blurring of key features.Even if it contains a wealth of information,it is difficult to adapt to the human eye.Therefore,it is necessary to use image processing technology to enhance the industrial ray image,improve the brightness contrast,and retain the detailed information in the original image,so as to meet the needs of industrial applications.From the perspective of practicability,this paper expounds the development of industrial image enhancement at home and abroad in recent years,analyzes the effects of different algorithms on industrial X-ray image enhancement,and makes a comparison and quantitative analysis.The similarities and differences of the algorithms and the remaining shortcomings are summarized.In addition,improved methods are also proposed on the algorithm of the experiment to further enhance the enhancement effect.The specific research content is divided into three parts:1.Use traditional image enhancement methods for industrial images,such as histogram equalization and limited contrast adaptive histogram equalization.They can adjust the image brightness and contrast,improve the display effect,and also use sharpening filters of different operators to enhance Feature outline.After comparing experiments,the performance and effect of traditional enhancement methods applied to industrial images were analyzed2.Improved image enhancement method based on Retinex theoretical model.This article mainly introduces the center surround theory in the late development of the Retinex model.After analyzing the shortcomings of this enhancement algorithm applied to industrial images,the internal transformation and mapping are improved.Finally,guided filtering is performed to remove noise while maintaining the contour edges.3.An industrial image enhancement method combined with local Laplace filtering is proposed.According to the characteristics of industrial images,this article designs a preprocessing of adaptive brightness adjustment to improve the overall brightness,and then through simplified local Laplacian filtering to maintain the quality of image texture detail enhancement,and also improve the contrast of the feature area.