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通过图像对图像的翻译,用于metalens成像的metalens风格的图像合成
Chanik Kang1, Hyewon Suk2, Joonhyuk Seo3
1Department of Artificial Intelligence, Hanyang University, Seoul, 04763, Republic of Korea.
Scientific reports
|January 20, 2026
概括
这项研究引入了一种用于生成合成金属图像的新方法,大大减少了数据收集时间. 这种方法有效地训练了偏差校正模型,克服了金属体成像研究中的一个关键瓶.
科学领域:
- 光学和光子学 在光学和光子学.
- 计算成像技术的成像
- 机器学习 机器学习
背景情况:
- 金属镜片提供超薄的光学,但患有色谱和取决于场的偏差.
- 目前的深度学习偏差校正需要广泛的配对图像数据集,造成数据瓶.
研究的目的:
- 开发一种数据效率高的方法来纠正金属的偏差.
- 合成现实的metalens风格图像用于训练偏差校正模型.
主要方法:
- 制定了异常合成作为一个metalens-conditioned图像到图像转换问题.
- 在配对的metalens和常规图像上训练了一个发电机网络.
- 使用生成器将普通照片转换为具有现实偏差的金属式图像.
主要成果:
- 与基于变压器的基线相比,实现了LPIPS (VGG) ~62%的减少.
- 在约30秒内生成了600张合成图像,比真实金属的采集速度快得多.
- 证明,仅合成数据就足以训练有效的metalens图像恢复模型.
结论:
- 拟议的图像对图像翻译方法有效地合成了金属变异.
- 这种方法显著缓解了金属素成像研究中的数据瓶.
- 能够更快地开发和部署基于金属的成像系统.
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