Fig. 9

Impact of preprocessing an image with VEViD on object detection by a neural network (YOLO). When applied to the original image, YOLO identifies 5 objects. After preprocessing by VEViD, the same YOLO algorithm detects 15 objects without having to be retrained on low-light images. Middle image is preprocessed by the full VEViD whereas the bottom image is preprocessed with its simplified approximation (VEViD-Lite). The approximation has very similar visual quality but with much lower latency (Fig. 7). Parameter values for the full VEViD (middle image): S = 0.4, b = 0.5, G = 0.6, and for the VEViD approximation model (bottom image): b = 0.5, G = 0.6