Multilayer Bayes blind deconvolution method for SAR image based on frequency domain and spectrum matrix

Abstract

The invention discloses a multilayer Bayes blind deconvolution method for an SAR image based on a frequency domain and spectrum matrix, and the method comprises the steps: inputting and observing the SAR image g, and giving an observation model; initializing an original SAR image f and a point spread function h as f<0> and h<0>, and giving a prior model; initializing hyper-parameters of the model, setting a confidence value, and giving a prior model; carrying out the zero extending and cyclic shift of a mask, h and h<0> of the prior model as c<es>, h<es>, and h<0><es>, and carrying out the conversion of c<es>, h<es>, and h<0><es> and the image into the frequency domain; constructing and initializing a spectrum matrix through the spectrums of cycle covariance matrixes of f and h<es>; optimizing random distribution to iterate and estimate hyper-parameters, frequency domain h<es> and frequency domain f; converting the frequency domain to a spatial domain, carrying out shifting and zero removing, and outputting the final result of blind deconvolution. The method saves a process of vectoring and matrixing, so as to avoid high-cost superlarge matrix operation. The method employs the frequency domain to represent the vectors and matrixes, employs the spectrums of the matrixes to construct the spectrum matrix, achieves the deconvolution at low operation cost, and effectively improves the operation efficiency of blind deconvolution of the SAR image.

Description

technical field [0001] The invention relates to a multilayer Bayesian blind deconvolution method for SAR images based on a frequency domain and a spectrum matrix, and belongs to the technical field of remote sensing image processing. Background technique [0002] Synthetic Aperture Radar (SAR) image deconvolution is an economical, feasible and effective technique for improving the resolution of SAR images. The multi-layer Bayesian blind deconvolution evolved from it is based on setting the original SAR image prior model, point spread function (PSF) prior model, noise prior model and model parameter prior model, A method for joint estimation of original SAR image, point spread function and model parameters based on observed images. This method does not need to obtain PSF and model parameters in advance, and gets rid of the necessary conditions for traditional SAR image deconvolution. However, multi-layer Bayesian blind deconvolution of images is usually implemented through ...

AI Technical Summary

Problems solved by technology

However, multi-layer Bayesian blind deconvolution of images is usually implemented through matrix operations. Before the operation, the image must be vectorized, the operator should be matrixed, and the vector should be converted back to the image after the operation, which undoubtedly increases the image processing time.

What's more, the SAR image observation scale is very large, and its data volume is higher than that of ordinary images, and the high data volume will form a very large matrix (for example, for a SAR image with a size of 1000×1000, the dimension of its operator matrix will be as high as 1000000×1000000), if the matrix operation method is still used, the amount of storage and calculation will increase exponentially, and the storage capacity and operation time of the computer will be greatly consumed, thereby reducing the efficiency of blind deconvolution of SAR images

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