在影像医学图像的诊断中,为了能更好地挖掘出尽可能多的决策信息,需要对图像进行有效的图像增强,而传统的医学图像增强算法具有噪声和模糊性的缺点,因此,提出一种基于剪切波和改进Pal-King算法的图像增强算法。首先利用剪切波变换将图像分解为高频和低频两部分,然后通过自适应阈值去噪的方法对图像进行有效去噪,再使用剪切波反变换重构图像,最后,使用Pal-King算法对图像进行对比度增强,以突出图像的细节信息。为了验证算法的有效性,利用自建图片库将本文算法与剪切波、分数阶微分以及改进的Pal-King增强方法进行比较,结果表明,本文算法处理的图像在增强效果和对比度方面都有了显著的提高。

In the process of diagnosis using medical image, it is necessary to do image enhancement efficiently in order to mine more information for decision-making as much as possible. However, the traditional medical image enhancement algorithm has some shortcomes, such as creating noise and fuzziness. Therefore, an image enhancement algorithm based on shearlet domain and improved Pal-King algorithm is proposed. First, the shearlet transform is used to decompose the image two parts, high frequency part and low frequency part. Then the adaptive threshold denoising method is used to denoise the image efficiently. After that, the inverse shear wave transform is used to reconstruct the image. Finally, Pal-King algorithm is used to enhance contrast to highlight the details of the image. In order to verify the validity of this algorithm, the processing results of the proposed algorithm are compared with shear wave, fractional differential and the improved Pal-King enhancement method respectively by using the self-built image database. Results show that both the enhancement effect and contrast of image by the proposed algorithm has significant improvements.