光学
编码孔径
傅里叶变换
摄影术
合成孔径雷达
光圈(计算机存储器)
孔径合成
图像质量
光谱成像
动态范围
信噪比(成像)
物理
计算机科学
干涉测量
衍射
人工智能
图像(数学)
探测器
声学
量子力学
作者
Bowen Wang,Sheng Li,Qian Chen,Chao Zuo
出处
期刊:Optics Letters
[The Optical Society]
日期:2023-01-02
卷期号:48 (2): 263-263
被引量:14
摘要
In this Letter, we report a new long-range synthetic aperture Fourier ptychographic imaging technique, termed learning-based single-shot synthetic aperture imaging (LSS-SAI). LSS-SAI uses a camera array to record low-resolution intensity images corresponding to different non-overlapping spectral regions in parallel, which are synthesized to reconstruct a super-resolved high-quality image based on a physical model-based dual-regression deep neural network. Compared with conventional macroscopic Fourier ptychographic imaging, LSS-SAI overcomes the stringent requirement on a large amount of raw data with a high spectral overlapping ratio for high-resolution, high signal-to-noise imaging of reflective objects with diffuse surfaces, making single-shot long-range synthetic aperture imaging possible. Experimental results on rough reflective samples show that our approach can improve the peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) by 10.56 dB and 0.26, respectively. We also demonstrate the single-shot ptychography capability of the proposed approach by the synthetic aperture imaging of a dynamic scene at a camera-limited speed (30 fps). To the best of our knowledge, this is the first demonstration of macroscopic Fourier ptychography to single-shot synthetic aperture imaging of dynamic events.
科研通智能强力驱动
Strongly Powered by AbleSci AI