A series of psychophysical trials is conducted to study the human perception of images of various shapes that are degraded by various combinations of Gaussian blurring, sampling, and fixed pattern noise. These images were created using computer programs designed to simulate the types of image degradation associated with thermal imagers that employ focal plane arrays (FPAs) of detectors. A total of 46 observers participated in these trials, during which over 130,000 observations were recorded. The responses collected during the trials are used to develop an empirical model of human vision applicable to 2-D images having a gray scale, but not including other colors. This model can be used to predict the probability of a human observer correctly distinguishing between images of two different objects.

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Non‐birefringent photopolymer materials incorporated into Liquid Crystal Display (LCD) assemblies can improve performance characteristics such as angle of view (AOV), definition, and brightness. Such improvements can be made both to displays that incorporate permanent backlights, and also to those that rely in part on ambient lighting. This paper describes the optical characteristics of some non‐birefringent photopolymer materials, their incorporation into displays, and the consequent improvement in display performance.

A computer model has been developed to predict the probability of recognition of particular shapes when viewed through a thermal imager employing either scanned or focal plane array detectors.This model is based on the results of a series of psychophysical trials during which human observers have considered over 120,000 images of shapes having a range of initial contrasts, and which have been degraded by various combinations of blurring and sampling. These computer generated images were presented to the observers in a random order and with a random degradation, using programmes to select images and display them on a computer monitor. After each presentation the observer decided which was the most likely shape to represent the image displayed on the screen. The responses collected have been used to calculate the human recognition probability of each image. A correlation has been found between the probability of recognition of any specified degraded shape and the relative contrast between the image of that shape, and the image of a similarly degraded circle of the same area. This model has been extended to include the effects of fixed pattern noise and applied to simplified images of cars and vans.