Reducing motion blurring associated with temporal summation in low light scenes for image quality enhancement
Gabriel Zahi, Shigang Yue · 2014
In order to see under low light conditions nocturnal insects rely on neural strategies based on combinations of spatial and temporal summations. Though these summation techniques when modelled are effective in improving the quality of low light images, using the temporal summation in scenes where image velocity is high only come at a cost of motion blurring in the output scenes. Most recent research has been towards reducing motion blurring in scenes where motion is caused by moving objects rather than effectively reducing motion blurring in scenes where motion is caused by moving cameras. This makes it impossible to implement the night vision algorithm in moving robots or cars that operate under low light conditions. In this paper we present a generic new method that can replace the normal temporal summation in scenes where motion is detected. The proposed method is both suitable for motion caused by moving objects as well as moving cameras. The effectiveness of this new generic method is shown with relevant supporting experiments.