欢迎您！东篱公司
退出
-
申报数据库
-
立项数据库
-
成果数据库
-
项目获奖数据库
中文摘要：
为了精确提取图像的细节特征，改善图像增强的效果，该文提出了一种基于形态学的可变权值自适应增强算法。通过构造扩展全方位多尺度结构元素，并进行top-hat变换，分解了图像不同方向不同尺度的细节。该文打破了传统形态学增强算法中各方向细节取均值融合的思想，实现了根据图像的局部灰度特性，动态调整各方向不同尺度细节的权值。在图像增强过程中，根据提取到的细节的结构化特征，构造相应的自适应增益函数，从而实现图像的自适应增强。实验结果表明，该算法能较好地利用图像的自相关性，比传统的形态学增强方法更有效地突出图像的细节信息，且能抑制噪声的放大。
英文摘要：
In order to extract accurately the image details, and improve the effect of image enhancement, an adaptive image enhancement algorithm with variable weighted matching based on morphological is proposed. With this method, extension omni-directional multi-scale structure element is constructed, which is used to decompose image of different scale details in different direction through top-hat translation. The proposed algorithm brokes the idea of that the detail weighted in each direction is taken average in traditional morphology method, and adjusts the weight of the different detail direction based on the dynamic characteristic analysis of the local gray level. In the image enhancement process, according to the structured feature of extracted details, the corresponding adaptive gain function is constructed to realize the image adaptive enhancement. The experimental results show that, the algorithm can highlight more effective image details than the traditional morphological method of image enhancement by using the autocorrelation of image, and can suppress the noise in some extent.
同期刊论文项目
同项目期刊论文
A statistical iteration approach with energy minimization to sinogram noise reduction for low-dose X
Regularized maximum likelihood algorithm for PET image reconstruction using a detail and edges prese
Application of regularized maximum likelihood algorithm in PET imagereconstruction combined with non
期刊信息
位置：