分类： 光学 >> 量子光学 提交时间： 2023-02-19
摘要： Complex-domain imaging has emerged as a valuable technique for investigating weak-scattered samples. However, due to the detector's pursuit of large pixel size for high throughput, the resolution limitation impedes its further development. In this work, we report a lensless on-chip complex-domain imaging system, together with a distributed-optimization-based pixel super-resolution technique (DO-PSR). The system employs a diffuser shifting to realize phase modulation and increases observation diversity. The corresponding DO-PSR technique derives an alternating projection operator and an enhancing neural network to tackle the measurement fidelity and statistical prior regularization subproblems. Extensive experiments show that the system outperforms the existing techniques with as much as 11dB on PSNR, and one-order-of-magnitude higher cell counting precision.
分类： 光学 >> 量子光学 提交时间： 2023-02-19
Liheng Bian
Haoze Song
Lintao Peng
Xuyang Chang
Xi Yang
Roarke Horstmeyer
Lin Ye
Tong Qin
Dezhi Zheng
Jun Zhang
摘要： Benefiting from its single-photon sensitivity, single-photon avalanche diode (SPAD) array has been widely applied in various fields such as fluorescence lifetime imaging and quantum computing. However, large-scale high-fidelity single-photon imaging remains a big challenge, due to the complex hardware manufacture craft and heavy noise disturbance of SPAD arrays. In this work, we introduce deep learning into SPAD, enabling super-resolution single-photon imaging over an order of magnitude, with significant enhancement of bit depth and imaging quality. We first studied the complex photon flow model of SPAD electronics to accurately characterize multiple physical noise sources, and collected a real SPAD image dataset (64 $\times$ 32 pixels, 90 scenes, 10 different bit depth, 3 different illumination flux, 2790 images in total) to calibrate noise model parameters. With this real-world physical noise model, we for the first time synthesized a large-scale realistic single-photon image dataset (image pairs of 5 different resolutions with maximum megapixels, 17250 scenes, 10 different bit depth, 3 different illumination flux, 2.6 million images in total) for subsequent network training. To tackle the severe super-resolution challenge of SPAD inputs with low bit depth, low resolution, and heavy noise, we further built a deep transformer network with a content-adaptive self-attention mechanism and gated fusion modules, which can dig global contextual features to remove multi-source noise and extract full-frequency details. We applied the technique on a series of experiments including macroscopic and microscopic imaging, microfluidic inspection, and Fourier ptychography. The experiments validate the technique's state-of-the-art super-resolution SPAD imaging performance, with more than 5 dB superiority on PSNR compared to the existing methods.
点击量
待评议
点击量
下载量
评论
