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Medical X-Ray Image Enhancement Based on Wavelet Domain Homomorphic Filtering and CLAHE

机译:基于小波域均匀滤波和克拉对的医疗X射线图像增强

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For the medical X-ray image with low brightness, low contrast and noise, we proposed an image enhancement algorithm which based on wavelet domain homomorphic filtering and contrast limited adaptive histogram equalization (CLAHE). Firstly, the image is decomposed by wavelet transformation, the image is decomposed into low-frequency and high-frequency coefficients of 1st layer of wavelet domain. Then the low frequency coefficients are processed by an improved homomorphic filter, and then linear amplified. The high frequency coefficients are processed by wavelet threshold shrinkage, and then the wavelet reconstruction is performed. Finally, the contrast limited adaptive histogram equalization (CLAHE) is used to modify the image's histogram, and the processing of the image is completed. The quality of image enhancement is carried on the subjective and objective evaluation, and compared with some other enhancement algorithms. Experimental results show that the algorithm can effectively enhance the texture detail of medical X-ray images, increasing the brightness and contrast, suppress noise, better than the general traditional enhancement algorithms.
机译:对于具有低亮度,低对比度和噪声的医疗X射线图像,我们提出了一种基于小波域同型滤波和对比度有限自适应直方图均衡(CLAHE)的图像增强算法。首先,通过小波变换分解图像,图像被分解成1ST的小波域的低频和高频系数。然后通过改进的均匀滤波器处理低频系数,然后加工线性。通过小波阈值收缩处理高频系数,然后执行小波重建。最后,使用对比度有限的自适应直方图均衡(CLAHE)来修改图像的直方图,并且完成图像的处理。图像增强质量在主观和客观评估上进行,并与一些其他增强算法进行比较。实验结果表明,该算法能有效提高医疗X射线图像的纹理细节,提高了亮度和对比度,抑制噪声,比一般传统的增强算法更好。

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