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使用哈达马德基的低采样单像素成像的显式压缩退化估计
Haoyu Zhang1, Jie Cao1,2,3, Chang Zhou1
1School of Optics and Photonics, Beijing Institute of Technology, Beijing, 100081, China.
Advanced science (Weinheim, Baden-Wurttemberg, Germany)
|November 30, 2025
概括
这项研究引入了一种新的技术,可以在单像素成像 (SPI) 中明确模拟压缩退化. 这种方法提高了图像重建质量,特别是在低采样场景中,为更广泛的SPI应用铺平了道路.
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
- 信号处理 信号处理
背景情况:
- 单像素成像 (SPI) 从1D测量获得2D图像,但质量受到样本数量的限制.
- 压缩传感 (CS) 可以从低样本的SPI数据进行重建,但明确的降解模型仍然不清楚.
- 现有的恢复方法依赖于隐式先验或数据驱动的方法,缺乏明确的压缩建模.
研究的目的:
- 介绍一种降解估计技术,用于在低采样SPI中显式压缩采样建模.
- 通过理解和考虑压缩诱导的退化,提高SPI重建质量.
- 为了证明拟议方法对动态场景和单像素视频成像的适用性.
主要方法:
- 开发了一种降解估计技术,用哈达马德基准模式在SPI中明确描述压缩采样.
- 提出了一种自我监督的学习方法来估计显式降解模型,主要是模糊内核.
- 研究了不同采样比率对压缩降解模型和SPI结果的影响.
主要成果:
- 压缩降解模型成功地进行了表征,并在不同的采样比率上得到反映.
- 显式模糊内核,随样本比例而变化,使用自主监督学习来估计.
- 数字和实验演示证实了SPI重建和视频成像方法的有效性.
结论:
- 拟议的降解估计技术为SPI的压缩采样提供了一个明确的模型.
- 这种方法通过考虑模糊内核,显著提高了低采样SPI重建.
- 该方法对推进SPI的实际应用,包括动态场景成像,显示出希望.
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