Abstract
A new approach to estimate the point spread function (PSF) of remotely sensed images is proposed here based on multiple natural pointlike sources, i.e., subimages of the observed image. First the conditional subimages are extracted, and then a blind deconvolution technique is used to derive the PSF from these subimages. For a sampled imaging system with signal-to-noise ratio 20 dB, this method can provide a relatively accurate PSF result. Moreover, the estimated PSF can be applied to image restoration using Wiener filtering or other nonblind restoration methods. The Authors.
Highlights
Spatial characteristics of remote sensing images are generally described by point spread function (PSF) for sampled imaging systems
The PSFs of spaceborne imagers are measured in laboratory before launch, they may change owing to vibration during launch or aging of materials over time
On-orbit PSF measurement is necessary to monitor the actual performance of spaceborne imagers and significant for further image restoration
Summary
Spatial characteristics of remote sensing images are generally described by point spread function (PSF) for sampled imaging systems. The majority of existing PSF estimation methods are developed based on the theoretical method proposed by Smith,[3] who aimed at calculating modulation transfer function (MTF, the magnitude of the Fourier transformation of PSF) from its knife-edge response These methods are frequently used for the reason that knife-edge or step profiles can be found in observed images, such as rooflines, farmlands, roadways, and tarps laid on the ground ahead of time.[4] In astronomical applications, reasonably bright, isolated stars can serve as point sources to derive the PSF.[5] for earth-observing satellites, PSF can be obtained by imaging a point source lying on the ground.[6] In this case, parametric models or multiple point sources are needed to rebuild the PSF. The last section is for the conclusion and final remarks
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