Liu Meng, Mei Shuli, Liu Pengfei, Gasimov Yusif, Cattani Carlo
College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China.
Huiying Medical Technology Co., Ltd., Beijing 100192, China.
Entropy (Basel). 2022 Nov 30;24(12):1754. doi: 10.3390/e24121754.
Because of noise interference, improper exposure, and the over thickness of human tissues, the detailed information of DR (digital radiography) images can be masked, including unclear edges and reduced contrast. An image-enhancement algorithm based on wavelet multiscale decomposition is proposed to address the shortcomings of existing single-scale image-enhancement algorithms. The proposed algorithm is based on Shannon-Cosine wavelets by taking advantage of the interpolation, smoothness, tight support, and normalization properties. Next a multiscale interpolation wavelet operator is constructed to divide the image into several sub-images from high frequency to low frequency, and to perform different multi-scale wavelet transforms on the detailed image of each channel. So that the most subtle and diagnostically useful information in the image can be effectively enhanced. Moreover, the image will not be over-enhanced and combined with the high contrast sensitivity of the human eye's visual system in smooth regions, different attenuation coefficients are used for different regions to achieve the purpose of suppressing noise while enhancing details. The results obtained by some simulations show that this method can effectively eliminate the noise in the DR image, and the enhanced DR image detail information is clearer than before while having high effectiveness and robustness.
由于噪声干扰、曝光不当以及人体组织过厚，DR（数字射线照相）图像的详细信息可能会被掩盖，包括边缘不清晰和对比度降低。提出了一种基于小波多尺度分解的图像增强算法，以解决现有单尺度图像增强算法的缺点。该算法基于香农 - 余弦小波，利用了插值、平滑性、紧支撑和归一化特性。接下来构建多尺度插值小波算子，将图像从高频到低频划分为几个子图像，并对每个通道的细节图像进行不同的多尺度小波变换。从而可以有效增强图像中最细微且对诊断有用的信息。此外，图像不会过度增强，并结合人眼视觉系统在平滑区域的高对比度敏感性，对不同区域使用不同的衰减系数，以达到在增强细节的同时抑制噪声的目的。一些模拟得到的结果表明，该方法可以有效消除DR图像中的噪声，增强后的DR图像细节信息比以前更清晰，同时具有高效性和鲁棒性。
