Abstract
Infrared texture is an important feature in identifying scenery. To simulate infrared image texture effectively at different distances, we propose a model of infrared image texture generation based on scenery space frequency and the image pyramid degradation principle. First, we build a spatial frequency filter model based on imaging distance, taking into account the detector’s maximum spatial frequency, and use the filter to process a “zero” distance infrared image texture. Second, taking into consideration the actual temperature difference of the scenery’s details due to variation of the imaging distance and the effect of atmospheric transmission, we compare the actual temperature difference with the minimum resolvable temperature difference of the thermal imaging system at a specific frequency and produce a new image texture. The results show that the simulated multiresolution infrared image textures produced by the proposed model are very similar (lowest mean square error=0.51 and highest peak signal-to-noise ratio=117.59) to the images captured by the thermal imager. Therefore, the proposed model can effectively simulate infrared image textures at different distances.
Highlights
Based on the image multiresolution pyramid principle, we propose an infrared image texture generation model based on scenery spatial frequency to generate infrared image texture at different distances
Where NETD is the noise equivalent temperature difference, SNR is the signal-to-noise ratio, SNRT is the threshold of the SNR, α × β is the instant field angle of the optical system, τd is the residence time, fp is the frame frequency, te is the integral time of the eye, Δf is the noise equivalent bandwidth, and MTFðfÞ is the modulation transfer function of the thermal imaging system[13] and is defined as MTFðfÞ 1⁄4 MTFo × MTFe × MTFd; (23)
Based on the principle of the multiresolution image pyramid, we proposed a new thermal infrared image texture generation model based on scenery spatial frequency
Summary
Infrared texture is an important feature in identifying scenery and has been used in various applications such as target detection, precision guidance, and three-dimensional scene simulation.[1,2,3] Infrared texture generation has been studied for decades, but because of security considerations, progress on the topic was seldom reported in the public literature. The final infrared image texture is obtained using the specific gray level and its deviation This simulation method can be adapted for a large-scale scene that needs only a low amount of detail, but it is not suitable for a scene that requires a high amount of detail because the infrared and visible textures have different principles of formation. The other simulation method based on a random field model, e.g., long correlation models[7] and the Markov random field model,[8,9,10] can generate infrared image texture This method requires a large number of model parameter tests to determine the proper parameters, and this method is highly complex and has low fidelity.
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