Scanning-based photon-limited imaging through scattering media

Imaging through scattering media has attracted considerable research interest in recent years. Most existing stud-ies are dedicated to reconstructing objects from the detected speckle pattern in an array-based system. However, the inevitable aliasing effect among pixels degrades the imaging quality significantly. Therefore, most existing studies usually could only work well under a weak scattering medium and a short working distance. To address this issue, we innovatively propose a robust scanning-based photon-limited imaging system for imaging through scattering media. To boost the imaging capability, we specifically adopt a point-to-point scanning strategy to eliminate the aliasing effect, and utilize a Geiger-mode avalanche photo diode to detect extremely weak signals after passing through the scattering media. As the images captured by the proposed imaging system always suffer from Poisson noise at unknown levels, we further construct a blind deep-learning based denoising framework with "one-to-all " strategy. By introducing a noise estimation sub-network with dense connection and batch nor-malization operations into the whole denoising framework, one trained model with synthetic datasets based on the realistic noise model could be generalized well to all real captured photon-limited images. Extensive experi-ments in real situations demonstrate that significant improvements can be achieved in the proposed method. For example, clear objects can be observed through several thick scattering media under an approximate working distance of 7 m, even with a low-power (average power of approximately 20 mW) light source.