Traditional Low Earth Orbit (LEO) optical satellite remote sensor MTF detection method is not suitable for the optical Earth observation satellites in Geostationary Earth Orbit (GEO) due to their high orbital height. Considering the low Ground Sample Distance (GSD) of the GF-4 satellite optical camera, it is difficult to set up large-size targets, combining features of GEO satellite gaze imaging, This paper presents a method to obtain MTF based on spatial random phase sampling of critical linear targets in multi-frame sequence images. The random change of satellite attitude was used to image the same target area for many times, and the resulting images were registered to the unified coordinate system, and target feature information was extracted. Then the Line Spread Function (LSF) was obtained by fitting the data, and finally the MTF curve was obtained by Fourier transform. The digital simulation analysis of the detection method was carried out, and the MTF detection of GF-4 satellite optical camera in orbit was realized through the actual setting of targets in the inspection field, which fully verified the effectiveness of the method.
A new model that can simultaneously do blind restoration and segmentation task is proposed in the paper. The new model belongs to the variant of Mumford Shah model. In order to promote the computational efficiency, the restoration part and segmentation part are decoupled from the original model. The blind image restoration part is based on the variable exponent regularizer to accurately estimate both piecewise constant point spread functions and smooth point spread functions. The segmentation part is the explicit edge indicator function obtained from the original model. The new model can be efficiently solved using split bregman framework. Numerical experiments show that the new algorithm produces promising results and robust to noise.
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