Recursive Video Denoising Algorithm for Low Light Surveillance Applications

We designed a video denoising algorithm for surveillance applications under low light conditions which is targeted to run on weak CPU. State of art algorithms don't meet these requirements because of huge memory bandwidth consumption. Among them, algorithms based on neural network have generalization issues especially when there are no references for training. Besides, the complexity of such methods is unaffordable in realtime embedding application. Hence, we propose three techniques, including: 1) adaptive noise strength estimation to fit into noise profile in real applications; 2) multi-resolution background segmentation inspired by human vision system, and 3) multi-pass denoise strategy. It's recursive and of first-order Markov property. Adaptive noise strength estimation also eliminates pre-calibration steps usually required by denoising algorithm and leads to easy deployment. Experiments show that our method can achieve better subjective denoising quality compared to state of art methods in target applications, especially in extremely low light scenes. Moreover, it requires small computation loads and small storage which makes it very suitable for implementation on weak CPUs.