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The staggered imaging camera is an important kind of remote sensing satellite camera. The staggered imaging technology can improve the spatial resolution of the camera without changing the focal length and pixel size of the optical system. However, the image resolution directly obtained by the staggered imaging camera is not enough. The existing traditional super-resolution methods have certain interpretability; the performance of deep learning super-resolution is related to the quality and quantity of training data set. There is no suitable data set for the remote sensing image generated by the staggered imaging system. So the frequency domain super-resolution technology is proposed for the image of the staggered imaging system. Low resolution remote sensing image can be regarded as the result of effective shift and sum of high-resolution image in time domain. There is a certain phase difference between low-resolution remote sensing image and high-resolution image in frequency domain. The time-shift property of two-dimensional image Fourier transform is used to find the difference between low-resolution image and high-resolution real image in frequency domain. The super resolution image is obtained by compensation coefficient. The frequency domain super-resolution algorithm is sensitive to noise. In order to suppress the noise interference, a special frequency domain filter is designed to filter the noise. It is verified by SPOT5 data that the frequency domain super-resolution can completely recover the noiseless image. Under the condition of image noise, the peak signal-to-noise ratio and the structure similarity can reach 35.754dB and 0.97 in 1.001 seconds.
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Xin Hou, Pu Wang, Wei An, "Frequency domain super-resolution of staggered imaging system," Proc. SPIE 11781, 4th Optics Young Scientist Summit (OYSS 2020), 117810Y (28 February 2021); https://doi.org/10.1117/12.2590635