The relationship between correct discrimination probability of the human eye and perceivable signal-to-noise (SNR) threshold is studied for different equilateral triangle sizes with specified luminance through combining theoretical calculation with practical experiment based on triangle orientation discrimination (TOD) performance evaluation method. Specifically, the simulation images of triangle patterns are generated by an infrared imaging system (IRIS) simulation model. And the perceivable SNRs for these images are calculated by establishing the system theoretical model and the human vision system model. Meanwhile, the Four-Alternative Forced-Choice experiment is performed. Experiment results of several observers are averaged statistically and the curves of perceivable SNR threshold which change with the correct discrimination probability are obtained. Finally, the analyses of these results show that these changes are in accordance with the psychometric function and that the fitting curves become steep with the increase of triangle sizes. These data and conclusions are helpful to modify the existing TOD performance model of an IRIS.
Most image data that we encounter is in color, thus measuring clutter in color images has become increasingly important. The extension of phase correlation to quaternion space, which can measure color similarity as well as the structural similarity between two color images, is defined. It is used to describe the global clutter in color images. The correlation degrees between the experimental probability of detection and that predicted by the clutter metric are presented. Experiment results show that the quaternion phase-correlation-based clutter metric can perform well in quantifying color image clutter.
One of the most challenging research topics in the field of target detection in electro-optical images is the relationship between image content and human detection performance. We propose two target structure similarity (TSSIM) metrics to describe global image clutter. Via a simple mathematical formula, the TSSIM clutter metrics and their combinations with different local target-to-background contrasts, which are loosely referred to as signal-to-clutter ratios (SCRs), are used to predict human detection performance. Other clutter measures and their combinations with the contrast ones are also considered for comparison. The degrees of correlation between target detection probabilities and various clutter metrics, as well as the SCR models, are presented. Experiment results show that the TSSIM measures are more suitable for quantifying electro-optical clutter than other clutter measures compared. And the SCR metric, which combines the root-sum-of-squares local contrast and a TSSIM clutter metric, generates the best predictive capabilities for the experimental detection probabilities.
Detecting a target out of its surrounding background is a major problem in various infrared seekers. The cluttered scenarios present extreme challenges for the modern IR seekers and are the focus of technology efforts. So, a complete performance evaluation model should include all of the following elements: i) the background ii) the target, iii) the atmosphere, iv) the sensor, and v) the image processing algorithms. Since the staring IR seeker systems are emerging as the latest application of the thermal infrared technology, it suffers the most from the lack of complete performance evaluation models.
We present a robust performance evaluation technique for staring IR seekers based on signal-to-interference ratio (SIR), with the quantitative description of the background clutter and detection algorithms emphasized. The power transfer functions of the optical system, detector and electronic are established to describe energy transmission of the signal and interference (noise and clutter) through the IR seeker, and the targets' radiant intensity statistics as well as the noise's statistical characteristics are also taken into account. In order to quantify the background clutter, we use a clutter measure based on its energy content - power spectral density (PSD). Based on this measure, a SIR is developed to analyze detection performance. Furthermore, the influence of several classical detection algorithms on the SIR is analyzed.
Micro-scanning is an important technique in high-resolution IR imaging field. It can increase the system resolution and improve the performance of imaging systems. For the design of super-resolution IR imaging system, it is necessary to choose the optimum micros-canning mode according to the particular fill factor of sensor. Hence, it is very important to study the effect of fill factor on the micro-scanning image quality. Under some assumptions, this paper introduces the sampling-averaging MTF of detector array at spatial Nyquist frequency as a figure of merit to quantitatively evaluate the improvement of different micro-scanning modes to image quality for different fill factors (1, 2/3, 1/2, 1/3) of infrared sensor. Finally, typical sampling imaging processes of focal plane array with the above fill factors are simulated. Experimental results qualitatively descript the effect of fill factor on the micro-scanning image, and show a good agreement with theoretical analysis.