Evaluating and predicting human detection performance of targets embedded in distorted and restored infrared images

The effects of atmospheric blur, gaussian noise distortion and Wiener filter restoration on human target detection performance is addressed in this paper. A specially designed psychophysical experiment shows significant degradation in detection performance caused by the contrast and noise limiting effects of the atmospheric blur and noise distortion. The experiment also shows that although Wiener filter restoration improves the distorted image sharpness and contrast, it amplifies the noise and does not compensate for the performance degradation caused by distortion. The paper presents a spectral analysis approach to the original, distorted and restored images, as well as an analysis of the target signal to noise ratio. Each analysis defines an alternative method for evaluating a spatial cutoff frequency that determines the restoration efficiency, which practically limits the detection performance. This frequency is then used to predict the probability of detection according to a target acquisition model and the Johnson chart. Moreover, the methods are used to determine whether noise or contrast will limit target perception. The methods were applied to the experimental infrared image database and showed good agreement with the related experimental probabilities of detection.