针对医疗图像增强的定向搜索优化纹理基于自适应性马校正 (DSOTAGC) 技术
Upendra Kumar Acharya1,2,3, Sandeep Kumar2
1Department of Electronics and Communication Engineering, Galgotias College of Engineering and Technology, Greater Noida, Uttar Pradesh India.
概括
这项研究引入了一种新的医学图像增强技术,使用定向搜索优化进行自适应性马校正. 它有效地减少了艺术品,并改善了对比度,而不会丢失图像信息.
科学领域:
- 医疗成像医学成像
- 图像处理 图像处理
- 计算机视觉 计算机视觉
背景情况:
- 医疗图像往往患有低对比度和复杂性,导致信息丢失和现有增强方法的工件.
- 传统技术可能会对结构相似性和峰值信号噪声比 (PSNR) 产生负面影响.
研究的目的:
- 为医疗图像增强提出一种新的定向搜索优化基于纹理的自适应性马校正 (DSTAGC) 技术.
- 通过减少文物和保存图像信息来克服现有方法的局限性.
主要方法:
- DSTAGC技术利用图像纹理来消除文物,通过自适应控制增强速率.
- 它采用修改后的加权概率密度函数和适应性玛参数来改善对比度.
- 使用定向搜索优化算法和结合和PSNR的健身函数来优化关键参数.
主要成果:
- 与其他算法相比,拟议的技术显示出优越的视觉质量和增强的信息内容.
- 它有效地抑制了文物,并显著改善了图像对比度,澄清了边缘和丰富了纹理.
- 评估证实了更好的噪声抑制和结构相似性保护.
结论:
- DSTAGC技术提供了有效的医疗图像增强,平衡视觉质量,信息保存和文物减少.
- 这种方法在挑战医疗图像数据集的现有技术上提供了显著的改进.
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