Electronic Imaging and Signal Processing Toward Realistic Night-Vision Simulation

摘要

A novel color-transformation method converts daytime scenes into night-vision goggle images suitable for realistic nighttime training. To date, nighttime training using flight or driving simulators has been hindered by the lack of typical night-vision goggle (NVG) rendering. NVG performance is often simulated in training vehicles by converting daytime images into black and white, and displaying the resulting scene in green. However, this approach does not consider that NVG images are fundamentally different from daytime scenes. For instance, elements such as plants and trees that contain chlorophyll appear bright in NVG images while they are dark in black and white. For effective training, the illusions and limitations associated with NVGs must be captured and retained. New methods have recently become available that enable the creation of sensor effects, such as noise and halos, around light sources. However, image intensifiers are particularly sensitive to near-IR (NIR) light, this information is often disregarded. In addition, reduced contrast and resolution cause NVG images to appear significantly different from daytime imagery. All of these effects give rise to misinterpretations and illusions. When conducting training on how to deal with these effects, it is essential to simulate the typical properties of NVG imagery.