Applied Sciences, Vol. 16, Pages 245: SNR-Guided Enhancement and Autoregressive Depth Estimation for Single-Photon Camera Imaging

Applied Sciences doi: 10.3390/app16010245

Authors:
Qingze Yin
Fangming Mu
Qinge Wu
Ding Ding
Ziyu Fan
Tongpo Zhang

Recent advances in deep learning have intensified the need for robust low-light image processing in critical applications like autonomous driving, where single-photon cameras (SPCs) offer high photon sensitivity but produce noisy outputs requiring specialized enhancement. This work addresses this challenge through a unified framework integrating three key components: an SNR-guided adaptive enhancement framework that dynamically processes regions with varying noise levels using spatial-adaptive operations and intelligent feature fusion; a specialized self-attention mechanism optimized for low-light conditions; and a conditional autoregressive generation approach applied to robust depth estimation from enhanced SPC images. Our comprehensive evaluation across multiple datasets demonstrates improved performance over state-of-the-art methods, achieving a PSNR of 24.61 dB on the LOL-v1 dataset and effectively recovering fine-grained textures in depth estimation, particularly in real-world SPC applications, while maintaining computational efficiency. The integrated solution effectively bridges the gap between single-photon sensing and practical computer vision tasks, facilitating more reliable operation in photon-starved environments through its novel combination of adaptive noise processing, attention-based feature enhancement, and generative depth reconstruction.

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