通过纠正空间变异异常的空中图像质量提升
Changyan Du1,2,3, Jihong Xiu4,5, Lin Sun1,3
1Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun, 130033, China.
Scientific reports
|October 13, 2025
概括
这项研究引入了一种新的算法,用于纠正空中摄像头的空间差异异异常引起的图像质量下降. 该方法使用Seidel异常导向模型和补丁智能解卷与Plug-and-Play预先进行有效的图像恢复.
科学领域:
- 光学和光子学 在光学和光子学.
- 图像处理 图像处理
- 计算机视觉 计算机视觉
背景情况:
- 空中摄像机由于复杂的操作条件而遭受空间变化的图像退化.
- 非静止和非均的点分布函数 (PSF) 复杂化了图像质量恢复.
研究的目的:
- 开发一个图像质量增强算法,用于空中图像中的空间变异异常.
- 应对动态和空间变化的PSF所带来的挑战.
主要方法:
- 引入了一个由西德尔偏差光学先验指导的空间变化的PSF模型.
- 使用西德尔系数来描述空间变化的受约束盲PSF估计.
- 采用补丁智能解卷结合PnP (Plug-and-Play) 之前进行高效的校正.
- 为了提高质量,利用了解卷和PNP规范化之间的代改进.
主要成果:
- 提出的方法显著优于现有的最先进的方法.
- 将图像恢复的代从8次减少到3次.
- 在可见光摄像头的NIMA (7.49%) 和HyperIQA (14.15%) 中取得了实质性的改进.
- 在红外摄像头方面取得了显著的收益 (29.58%和17.53%的改进).
结论:
- 开发的算法有效地纠正空中图像中的空间变异异常.
- 结合Seidel引导的PSF模型和PNP解卷的组合,提供了卓越的性能和效率.
- 该方法对改善可见光和红外光谱的图像质量有希望.
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