Study On The Algorithms For Industrial X-Ray Image Enhancing

With increasing demands in all sort of detections, X-ray inspection technology is widely used and played an important role in a variety of fields, such as medical diagnosis, safety examinations, aerospace and industrial inspections. During the imaging procedure for X-ray inspection system, however, the complexity of industrial components and unexpected factors,such as electrical and ray noise, result in poor image quality. The poor image quality,including low contrast and blurry in structural details, ultimately affect the judgment and recognition of component structure. To meet the specific criterion in practical applications, it is necessary to improve the quality of X-ray images, which facilitates the further judgment and recognition of the component structure. Image enhancement, as an approach to improve image quality, can highlight details, improve image contrast, as well as visualization efficiency. Therefore, the study on high-quality and fast X-ray image enhancement algorithms is of great significance for both image theory and practical application.For this purpose, we conducted extensive analysis on image enhancement algorithms,and summarized the deficiencies of current algorithms used for practical application. Based on the summary, we proposed a few of new methods, with which the improved performance was achieved. The paper is outlined as follows:(1) With analysis on anisotropic diffusion model of partial differential equation, an adaptive anisotropic image enhancement algorithm is proposed based on local variance. First,the algorithm uses local variance to detect image edge feature. Then, the variance threshold is set, with which the error is decreased with the increase of iteration number. As for classification of image features, the positive diffusion is used in the flat region containing noise, to effectively remove the noise. The reverse diffusion is used in the edge detail area, to enhance the characteristics of the image. Experimental results show that the proposed algorithm can overcome the limit of the Laplace enhancement algorithm in high noise sensitivity, and effectively enhance the image details.(2) To overcome the limits that the image gradient is sensitive to noise, we proposed an improved method of contrast field enhancement. Instead of using the image gradient, we proposed an amplification algorithm based on variational method. In this algorithm, the amplification coefficient is a monotonous decreasing function of the difference curvature.Additionally, more neighboring points are taken into account when calculating the amplification coefficient. Specifically, the proposed amplification algorithm involves not only the gradient component in four directions, but also another four pixels in neighbors. As the results, the amplification algorithm is less sensitive to noise. Experimental outcomes on standard testing image and industrial X-ray image showed that the proposed algorithm generates the enhancement in low contrast, the sharpness in edges, as well as the reduction in noise. Those experiments demonstrate the advantages of proposed algorithm in detail protection and noise compression.(3) With extensive analysis of the principle and characteristics of non-local mean filtering algorithm, we proposed an image enhancement algorithm based on non-local difference information. Through combination with the classical image sharpening algorithm,the new algorithm adopts the idea of non-local mean value, in order to calculate the weight value of the neighborhood pixels in the enhancement operation, wherein the neighborhood size is changeable. Meanwhile, the weight of the center pixel value is adjustable, reflecting the effect of high upgrade. Therefore, the new algorithm keeps the advantages of non-local mean filtering algorithm and high boost image enhancement algorithm. Moreover, we analyzed, through experiment, the influence of related parameters on the performance of the algorithm, as well as summarized the principle for choosing the parameters. In addition,experiments are carried out on the traditional testing image and the actual acquisition of industrial radiographic image, with aim to compare with the performance of the same kind of algorithm. Experimental results show that the proposed algorithm generates satisfactory outcomes in enhancing the image details and maintaining a high signal-to-noise ratio of peak value.(4) With extensive analysis of the traditional Retinex algorithm, an improved algorithmis proposed based on HVS. Specifically, we analyzed the effects of single scale algorithm and multi-scale algorithm on image enhancement. Different enhancements were achieved with selection of different parameter values. Because S function model is more compatible with characteristics of human vision, and because it can overcome the exceeding range of image display by using log processing model, we introduced nonlinear operation into the image enhancement by using the S function model. Beyond that, the algorithm also improves the image in two aspects: protection of the image brightness and reduction of beam noise effect on image. The experimental results show that this method not only enhances the common gray image, but also enhances the X-ray image with low contrast. As the results, this method meets the requirements of the thereafter analysis procedure for the X-ray image.