KEYWORDS: Modulation transfer functions, Minimum resolvable temperature difference, Signal to noise ratio, Spatial frequencies, Thermography, Contrast transfer function, Imaging systems, Fourier transforms, CCD cameras, Systems modeling
The edge response is one of many techniques used to calculate the MTF (Modulation Transfer Function) of an imaging system. The MTF can be used to calculate the MRTD function (Minimal Resolvable Temperature Difference) for thermal imagers or the MRC (Minimal Resolvable Contrast) function for visible image systems. Most of the conventional techniques used to calculate the MRTD or MRC functions are time consuming and can be influenced by the subjectivity of the operator. A comparison of conventional MRTD measurements for more than 300 systems is compared with the MRTD function derived using the edge response technique.
This paper presents an improved automated method for calculating the MTF (Modulation Transfer Function) of an optical system. The paper presents the theoretical background and describes various techniques for calculating the edge response and MTF. An improved method is proposed and it is shown to be more accurate and robust. An additional advantage of the proposed method is that it can be fully automated. The proposed method is valid for various optical imaging systems. The results are compared and conclusions are made regarding the validity of the technique.
In the development and analysis of sophisticated detection systems, the ability to simulate background clutter provides a useful tool in assessing system performance. Infrared cloudy sky images can be generated using a technique based on the physical parameters of the background. Power spectra and radiance distribution functions for ground-based IR cloudy sky images in 3 to 5 μ and 8 to 12 μ spectral windows were determined experimentally in our previous research. The empirical power spectra and radiance distribution functions are used here as a basis to generate computer simulations of cloudy sky images. We require that the power spectrum and radiance distribution functions of the simulated image be in agreement with the functions that we obtained experimentally in our previous research. Realistic cloudy sky images were obtained. The simulated cloudy sky images retain the radiance distribution and power spectra functions of real cloudy sky IR images.
In the development and analysis of sophisticated IR detection and recognition systems it is necessary to have a priori knowledge of the background clutter. The spatial power spectrum and the spatial autocorrelation functions are used to analyze the spatial structure of ground-based infrared cloudy sky images. The experimental results obtained for ground-based IR cloudy sky images do not fit the analytical model of the Wiener spectrum that is frequently used to describe natural clutter sources in the infrared. The spatial structure of ground-based cloudy sky IR images was found to be dependent on the percentage cloud cover in the image. A corrected model is developed that relates the spatial structure of cloudy sky images to the percentage cloud cover in the image.
In the development and analysis of sophisticated JR detection and recognition systems it is necessary to have a priori knowledge of the background clutter . In this paper, the spatial structure of ground-based infrared cloudy sky images is analyzed in terms of the spatial power spectrum and the spatial autocorrelation function . The experimental results we obtained for ground-based JR cloudy sky images do not fit the analytical model of the Wiener Spectrum 1,2 that is frequently used to describe natural clutter sources in the infrared .The spatial structure of ground-based cloudy sky IR images was found to be dependent on the percentage cloud cover in the image . Acorrected model was developed that relates the spatial structure of cloudy sky images to the percentage cloud cover in the image.
The statistical behavior of ground-based IR cloudy sky images are analyzed to acquire a priori knowledge of the background necessary for the development of sophisticated infrared target detection and recognition systems. Infrared cloudy sky images can be automatically segmented into their related groups of interest, according to the radiance statistical distribution, by implementing specialized image processing techniques. Once the images have been segmented, the cloud cover, statistical distribution, and other parameters of interest are readily obtained. It was found that, in the 8- to 1 2-μm spectral window, cloudy sky images must be divided into at least five regions of interest, and in the 3- to 5-μm spectral window, a distinction must be made between shaded cloud images and sunlit cloud images. Shaded cloud images have only three regions of interest, whereas sunlit cloud images are more complex and have at least five regions of interest. If each region is approximated by a Gaussian distribution, then the normalized cross-correlation function ofthe measured data with the multinormal function gives a value in excess of 0.9.
The statistical behavior of irradiance over a ground-based IR sky image is dependent on the angle of elevation at which the image was made. Images made at different elevation angles cannot be compared unless a correction is made for the different viewing angles. The angle-dependent radiance caused by the difference in the optical path length over the field of view of the image must also be corrected, otherwise incorrect conclusions will result. Once the corrections have been made, the image can be analyzed and the true statistical parameters of the image can be obtained.
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