Near-eye displays–displays positioned in close proximity to the observer’s eye–are a technology continuing to gain significance in industrial and defense applications, e.g. for augmented reality and digital night vision. Fraunhofer IOSB has recently developed a specialized measurement setup for assessing the display capabilities of such devices as part of the optoelectronic imaging chain, with the primary focus on the Modulation Transfer Function (MTF).

The setup consists of an imaging system with a high-resolution CMOS camera and a motorized positioning system. It is intended to run different measurement procedures semi-automatically, performing the desired measurements at specified points on the display.

This paper presents the extended work on near-eye display imaging quality assessment following the initial publication. Using a commercial virtual reality headset as a sample display, we further refined the previously described MTF measurement procedures, with one method being based on bar pattern images and another method using a slanted edge image. Refinements include improvements to the processing of the camera images as well as to the method of extracting contrast measurements. Furthermore, we implemented an additional, line-image-based method for determining the device’s MTF.

Target detection is a crucial task in defense applications such as surveillance, infrared search and track, and missile approach warning systems. Typically, the target image is extended over a few sensor pixels of the imaging system only and the detection is performed by appropriate algorithms.

In order to study the impact of imaging system design parameters and environmental conditions on the detection performance, a simulation tool is developed. Apart from computing detection ranges based on the expected signal to noise ratio the algorithm requires for detection, the tool is also meant for simulating image sequences of engaging targets. Therefore, it provides means to investigate the interplay between system design parameters and algorithms for target detection.

The simulation is based on a rigorous calculation of the target image in the focal plane, with consideration of the optical transfer functions of imaging chain components. Integrating the target image over the active pixel areas yields the additional signal of the detector pixels caused by the target. Based on these values and average background noise the signal to noise ratio (SNR) is obtained as function of the target distance. Image data is generated by overlaying the additional signals over a background image.