Deep Unfolding-Based Image Reconstruction For Quanta Image Sensors

Quanta image sensors are an emerging type of image sensor offering single-photon sensitivity. In this paper, we propose a deep unfolding-based image reconstruction method that integrates an alternating direction method of multipliers optimization with a total variation prior. The proposed method effectively combines model-based approaches with deep learning, providing enhanced interpretability and performance. Experimental results demonstrate that our approach outperforms conventional methods based on the observation model for quanta image sensors.