Industrial X-Ray Image Enhancement Algorithm Based on AH and MSR

An X-ray image enhancement algorithm based on AH (adaptive histogram) and MSR (Multi-scale Retinex) algorithm is proposed in this paper for the industrial X-ray image, which contrast is low, and the detail features is poor. Firstly, the contrast limited adaptive histogram equalization and neighborhood algorithm is used for the image. Then the mapping is built between the image and the detail scales by the enhance function ratio rules, which is adjusted by the local contracting information. Finally, according the enhance function radios, the reconstructed image is rebuild. Compared with other image enhancement algorithms, experimental results show that our algorithm can improve the global image effectively, moreover it overcomes the visible artifacts of X-ray image. Therefore, the x-ray image becomes clearer, and a better perceptual image is acquired for the image feature recognizing and matching.


Introduction
The valid method to enhance X-ray image is histogram enhancement.The histogram of industry X-ray in a small filed, most of pixels in the low frequency areas, and there are hardly few pixels in high frequency areas, Even the industry X-ray image is an image seems like the black image.Therefore, it is need a method to image enhancement.Recently, many people researcher on this field.Shu Yang and Cairong Wang proposed an image enhancement algorithm based on multi-scale morphological reconstruction [1].Stephane G. Mallat researches the theory for multi-resolution signal decomposition [2].Pizer S. M. proposed adaptive histogram equalization and its variations [3].Tang Jinshan adopted a direct image contrast enhancement algorithm in the wavelet domain for screening mammograms [4].Wang Xiu-bi [5] refers to image enhancement based on lifting wavelet transform [6].Jinshan Tang and Qingling Sun used contrast measure in the wavelet domain for screening mammograms image enhancement algorithm.In this paper, we will investigate image-enhancement technology based on adaptive histogram and wavelet.

Histogram Enhancement Theory
Histogram enhancement, the advantages of high speed and better effect, wildly applied in X-Ray processing.Histogram is the function of gray levels, which denotes the gray level of every pixel.Therefore, the contrast radio will be improved by gray nonlinear transform to adjust the accumulation function, and the gray in small range will be transform in the whole filed.
Image histogram is an important tool to analysis image gray distribution, which is a summary graph showing a count of the data points failing in the data.Therefore, histogram is defined as follows.

 
which n is the total pixels of X-ray image, is the gray level of corresponding pixels.
The X-ray is the low contrast, which most pixels are in the same gray level, thus it is hard to recognize.Histogram equalization is a method to improve image contrast it built a nonlinear transfer function to transform the his-togram of original image.The processed image with equal gray scale in the same gray level, at the same time, the maximum entropy is acquire, it means that the image information is abundant.
Hypothesis that the gray transfer function is The relationship between where

AHE Algorithm
Compared with the whole histogram equalization, the ee It is n ock, Definite the limit function to limit the gray level probability density, and adjust the exceed histogram, Figure 1 shows the processing.
To decrease the block effect, rpolation.The block gray value is the gray transform function of adjacent pixels.
Figure 2 depicts the bilin   , x y is the center of the sub image, compute the arle in adjacent areas, the value is the following: is the gray transform function,  

X-Ray Image Enhancement Algorithm
Multiple Scale Retinex algorithm used to industrial X-  and ndard i is the weight function.The different sta W io deviat n of normal  is selected by the i F function to control the scale r ge of the environme function, the parameter i an nt F with the large, middle and small scales.And the weight is basis for dramatic range and color sense [7].

X-Ray Image Enhancement Algorithm
The main idea of adaptive histogram and MSR is adjust put image is blocked into sub images, then we ad

Based on Adaptive Histogram and MSR
the original image histogram, then bilinear difference is used to rebuild, and the gray scale vale and position information is recorded.After coordinate transformation, the image is divided three sub images in three scale, the MSR enhancement will modify the amplify ratio, at the same time, the image will be a better dramatic range and effects.
The in just the gray value, the original limited function is 0.001, and the image is divided into three scales.Most of image information are in low contrast area, the ratio  is acquired by the Equation ( 6), and the transform ratio will be acquired.According to the CLAHE algorithm, B equalization every sub images, computed every the global MSE, the result is used to adjust limit function.
The Gauss transform ratio is acquired by the Eq The final transform ratio B is the following.
which the weight ratio is acquired by the Equation ( 9), the Equations ( 8) and ( 9) is built the relationship between the input and output images.
We will get three Gauss ratios in three scales, the image convolution in every scale, the Gauss filter is recursive, the input and output images will be acquired by the forward and the backward filter, at the same time, the relationship of the input data   in n and out put Equation ( 8) is the weight mean of every scale, the w

Experiments and Conclusions
Figure 3 is the picture of CJ10-40 AC contractor, which can see th the Figure 3(g), we see that the gray field is en picts the relationships between the time and th ture of JQX-10F3Z AC contractor, w of MSE and times of diffe

Conclusions
Industrial X-ray widely applied in nondestructive test-eight is 1/3, the output image will be acquire through the Equations ( 9) and (10).
size is 2816*300 pixels, CJ10-40 AC contractor has an important role in power system, so it is meaningful to enhance X-ray image in non-contract detection.
Figure 3(a) is the original input image, we at the contrast is very low that it is hardly to see anything in the Figure 3(a).Figure 3(c) is the MSR results, the processing time is 15.9787 s.The enhancement is poor, the processing image also with low contrast, and hardly to recognize in background.Figure 3(e) is the HE adjust enhancement results, the image contrast is enhanced slightly.is the algorithm results.The elapse time 4.68264 s. the image contrast is enhanced.Figure 3(i) is our method, we see that the gray field is enlarged.From the results, we can conclusion that our algorithm can improved the global contrast, at the same time, it is obviously that image enhanced between the detected element and background.
Table 1 de e MSE of test CJ10-40 X ray image, from the Table 1, we will see that our method is better in MSE and time than other methods.
Figure 4 is the pic hich size is2816*1000 pixels, JQX-10F3Z contractor also has an important role in power system, Figure 4 is the processing of the test 2nd.The 2nd test is the element with complex inner structure.
Table 2 describes the value rent methods.Compared with the different methods in the list, our method has a better result in MSE and the processing time is acceptable.ing，Industrial X-ray image has the characteristics of low contrast and more details, thus it is need to an image enhancement algorithm.To enhance the image contrast,    ve histogram equalization algorithm is used the first step.We set the initial limit coefficient of limit function then building a map between the image and the detail scales by the wavelet ratio, which is adjusted by the local contracting information.According the enhance function radios, the reconstruct image is rebuild.
Compared with other image enhancement algorithms, experimental results show that our algorithm can improve the global image contrast effectively, moreover, restrain the background and enhance the contrast between the background and the detection element.Processed image, with more details information and better vision, benefits to further identify and recognize.

2. 3 .
Limited Contrast AHE Algorithm AHE algorithm improve the image contrast, at the same tep of AHE algorithm is input image is constant.After transformed, every pixel of the image is tensile to extend the image gray level areas, and the vision effect is improved.adaptive histogram enhancement (AHE) has the advantage of good local contrast.But the AHE algorithm will commute the local histogram and accumulation distributing function of every pixels, it is extremely computational intensive.Besides, the AHE algorithm is sensitive for noises, which is liable to bad enhancement for local area.time magnified the noise.Even the enhancement will lead to image distortion in some detail area, which lead to noise amplification and distortion, that affects the image diagnose.The first s   , A x y block.And the local histogram in every block.limit contrast function to AHE in every block to Thus ge d to preliminary adjustment before the image bl it is need to bilinear inte ear interpolation processing, nerate transform function respectively.Then the adaptive bilinear interpolation used to joint output image   , B x y .

Figure 3 (
g) is the ALE algorithm results.From larged in different field.But the back ground of the Figure3(g) is complied mixed with noises.

Figure 3
Figure 3.The Input image