Iterative heavy weighted blind deconvolution method of passive millimeter wave radar image

A millimeter-wave radar and blind deconvolution technology, which is applied in image enhancement, image analysis, image data processing, etc., can solve the problem that image edges and detail information cannot be restored, and achieve good constraints and good stability

Inactive Publication Date: 2017-03-15
XIDIAN UNIV
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Problems solved by technology

[0004] In view of the above problems, the present invention provides an iterative reweighted blind deconvolution method for passive millimeter-wave radar images, which solves the problem that the prior art is sensitive to strong noise and mixed noise during blind deconvolution of passive millime

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  • Iterative heavy weighted blind deconvolution method of passive millimeter wave radar image
  • Iterative heavy weighted blind deconvolution method of passive millimeter wave radar image
  • Iterative heavy weighted blind deconvolution method of passive millimeter wave radar image

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[0066] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some, not all, embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.

[0067] The adaptive selection of regularization parameters in the variational regularization model is an inevitable requirement for the proposed blind deconvolution method to be practical. Therefore, an adaptive parameter update formula that does not rely on manual selection must be designed according to the proposed algorithm.

[0068] An embodiment of the present invention provides an iteratively reweighted blind deconvolution method for passive millimeter-wa...

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Abstract

The invention belongs to the radar image processing technology field and discloses an iterative heavy weighted blind deconvolution method of a passive millimeter wave radar image. The method comprises the following steps of (1) integrating an adaptive weight constructed based on a residual error into a data item; (2) integrating a spatial adaptive weight of a coefficient of an image under a bilateral total variational operator effect into an image regularization item; (3) applying a smoothness constraint under a Laplace operator effect on a passive millimeter wave imaging system point expansion function; (4) realizing image and point expansion function combination iteration blind deconvolution under a multi-scale coarse-to-fine framework; and (5) deriving an adaptive estimation formula of a model regularization parameter. In the prior art, during passive millimeter wave image blind deconvolution, a radar is sensitive to a loud noise and a mixing noise and can not recover image edge and detail information. By using the heavy weighted blind deconvolution method of the invention, the above problems are solved.

Description

technical field [0001] The invention belongs to the technical field of radar image processing, in particular to an iterative reweighted blind deconvolution method for passive millimeter-wave radar images. Background technique [0002] Passive mmWave can penetrate fog, cloud and rain, and can image 24 / 7, so it has a wide range of applications in navigation, guidance and surveillance. Passive mmWave images have relatively long wavelengths compared to visible and infrared images. In order to improve the spatial resolution, it is necessary to increase the aperture of the imaging antenna. However, due to platform constraints and many practical application requirements, large antenna apertures are subject to many limitations in practical applications. Therefore, the acquired observation images are generally highly blurred and have low spatial resolution. Furthermore, observation images inevitably suffer from degradation from systematic noise and natural clutter outliers. [00...

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Application Information

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IPC IPC(8): G06T5/00
CPCG06T5/002G06T2207/10044G06T2207/20004G06T2207/20028G06T2207/20192
Inventor 方厚章刘宏伟许述文潘东辉时愈刘立
Owner XIDIAN UNIV
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