通过扩散形式和群众搜索优化来提高微观图像质量
Subhash Chandra Patel1, Rajesh N Kamath2, T S N Murthy3
1School of Computing Science and Engineering, VIT Bhopal University, Bhopal, India.
Microscopy research and technique
|September 30, 2025
概括
这项研究引入了一种使用扩散工艺,swin变压器和优化的乌搜索算法来提高医疗图像质量的新无色化方法. 该技术显著提高了诊断准确度,特别是在肺炎等疾病中.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 图像处理 图像处理
背景情况:
- 医疗图像噪声降低了诊断准确度.
- 有效的消毒是至关重要的,特别是在资源较低的环境中.
- 由于图像相似性和数据稀缺性,肺炎诊断具有挑战性.
研究的目的:
- 为医疗图像引入一种新的无线化技术.
- 为了提高图像质量和可靠性,改善诊断.
- 用医学成像来解决肺炎等疾病诊断方面的挑战.
主要方法:
- 开发了一种基于swin变压器的新型扩散优化群众搜索算法.
- 预处理包括灰度转换,大小调整,规范化和高斯噪声添加.
- 该方法整合了扩散以减少噪音,swin变压器以捕获特征,以及乌搜索以优化超参数.
主要成果:
- 该方法实现了 38.47 dB 的峰值信号噪声比 (PSNR).
- 结构相似度指数 (SSIM) 达到98.14%,特征相似度指数 (FSIM) 达到0.980.
- 平均平方误差 (MSE) 为0.55,在四个不同的数据集中表现优于现有的排斥技术.
结论:
- 提出的方法有效地提高了医疗图像质量.
- 这导致了更精确,更可靠的诊断.
- 该技术显示出在各种医疗应用中改善诊断能力的巨大潜力.
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