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In recent years, with wide applications of high-sensitivity single-photon detectors, especially in the field of quantum imaging and optical imaging has achieved many important results, and low-light imaging technology based on single-photon level has gradually become an important branch in high-resolution imaging systems. Currently, the main single-photon detectors are single-photon counting avalanche diode (SPAD) sensors and support pixel arrays of different sizes, ranging from single-pixel detector sizes to tens of thousands of pixel SPAD arrays. The process structure of single-pixel SPAD detectors is relatively simple, and they are often used as the first choice for low-light imaging by virtue of their high sensitivity, small size, and low cost. However, single-pixel SPADs can only detect signals at a single location due to their lack of spatial resolution and cannot provide spatial information, and they are usually used in conjunction with a spatial light modulator DMD or SLM that possesses spatial resolution, to reconstruct 2D images through compressed sensing or quantum correlation. Although the single-pixel detector can provide ns-level or even ps-level temporal resolution, it is limited by the frame rate of the spatial light modulator (SLM). For example, the fastest digital micromirror device (DMD), a type of SLM, has a frame rate of 22 kHz. It means that the imaging rate of a single-pixel camera, which typically uses a SPAD and an SLM for single-photon imaging tasks, is usually limited to the order of seconds. This makes it challenging to significantly improve the imaging rate, especially when higher imaging resolutions, such as those exceeding hundreds of thousands of pixels, are required. Assuming that the imaged object is a fast-moving dynamic target, the imaging rate in the order of seconds will inevitably cause dynamic blurring, which also poses a challenge to the fast real-time performance of the single-photon imaging system.
The SPAD array sensor retains the excellent sensitivity, low dark count rate and high temporal resolution of single-pixel SPAD sensors. Due to the improvement of the fabrication process, multiple sensors and readout circuits are fabricated on the same chip, leading to the development of spatially resolved SPAD array camera. But the integrated design of SPAD arrays with multiple pixels and circuits inevitably causes cross-crosstalk between pixels. This crosstalk can significantly affect the accuracy of the signal. Additionally, the fill factor of such array camera is typically low. Although the fill factor can be improved by methods such as 3D stacking and microlens arrays, the utilisation of the space is still to be improved in comparison with that of single-pixel SPADs. However, it is undeniable that SPAD arrays perform well in high dynamic range photon flux detection, as well as high frame rate photon counting measurements by virtue of the parallel processing of multiple detectors, and the current commercial SPAD arrays have integrated hundreds of thousands of detector pixel units, which provide excellent spatial resolution. Unfortunately, due to the manufacturing process and the challenges in various aspects, the SPAD array camera has been used in high-quantification bit deep sampling in the mode to acquire high-resolution single-photon intensity imaging. Its exposure time is limited to the ms level. It is difficult to avoid dynamic blurring during the imaging exposure time when the motion frequency of the dynamic target reaches kHz or higher. Although the quantification bit depth can be sacrificed to shorten the minimum exposure time of the array camera to the ns level, the excessively short exposure time results in the SPAD array capturing the sparse photon data that is contaminate with a large amount of noise, necessitating the development of reliable photon denoising methods. These methods are essential for effectively separating background noise from the actual signals, thereby improving the signal-to-noise ratio of the imaging system. Therefore, the real-time performance of the imaging system at the expense of quantification sampling accuracy still needs to be further optimized.
To address the problem of limited imaging quality and rate of SPAD arrays under very short exposure time, this paper proposes a single-photon imaging enhancement deep neural network incorporating super-resolution deep learning, which can achieve high-fidelity reconstruction of low signal-to-noise single-photon images under ultra-short exposure time by constructing a single-photon image dataset with dynamic exposure time and performing adaptive training. In the experiments, it achieves the enhanced reconstruction of low-quality fan images (PSNR/SSIM, 6.54dB/0.18) under very low-light conditions with an exposure time of only 1μs and an average photon number of less than 0.5 photons (PNSR/SSIM, 13.21dB/0.34), and the images are effectively improved by +7.21dB/+0.16 for PSNR and SSIM. The passive remote enhanced reconstruction of an unmanned aerial vehicle at 5.19 km is achieved at an imaging exposure time of 5 μs, with an effective PSNR and SSIM enhancement of +4.78 dB/+0.2. This method provides a new technical solution for SPAD arrays to achieve ultra-fast-exposure high-quality imaging.-
Keywords:
- SPAD array detection /
- enhanced imaging /
- deep neural network /
- ultra-fast exposure
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