Compressed learning perception based SAR (Synthetic Aperture Radar) high-resolution image reconstruction method

An image reconstruction and high-resolution technology, applied in the field of image processing, can solve the problems of data calculation and memory resource waste, poor reconstruction effect and discarding of SAR high-resolution images, and achieve the effect of improving quality, shortening reconstruction time, and reducing correlation

Active Publication Date: 2011-08-03
XIDIAN UNIV
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Problems solved by technology

There are two disadvantages in such a processing method: first, since the sampling rate must not be lower than twice the signal bandwidth, this will bring great pressure to the hardware system; second, in the process of compression encoding, a large number of data, resulting in a waste of data computing and memory resources
However, in the existing SAR image reconstruction algorithms, the sparse bases used are all non-adaptive, such as Fourier base, wavelet base, etc. In general,

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  • Compressed learning perception based SAR (Synthetic Aperture Radar) high-resolution image reconstruction method

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[0021] Refer to attached figure 1 , concrete steps of the present invention include:

[0022] Step 1. Get training image collection

[0023] 1a) Input a group of high-resolution SAR images as training images, and divide them into small blocks with a size of q×q;

[0024] 1b) Transform the obtained small image blocks into column vectors, construct a set of training image sets X by permuting and combining the column vectors, and input a low-resolution SAR image Y.

[0025] Step 2. Use the Couple-KSVD algorithm to obtain the target training dictionary and coupled observation matrix

[0026] 2a) From the Couple-KSVD algorithm, give the total optimization formula s.t. ||α i ||0≤T, and input the training image set X, randomly initialize the target training dictionary Ψ and the coupled observation matrix Φ;

[0027] 2b) For the first objective function in the formula Transform to get:

[0028]

[0029] Among them, X is the training image set, Ψ is the target training ...

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Abstract

The invention discloses a compressed learning perception based SAR (Synthetic Aperture Radar) image high-resolution reconstruction method which is mainly used for solving the problem that the quality of a reconstructed image is reduced because a sparse base and an observation matrix cannot meet restricted isometry property (RIP) existing in the conventional method. The method comprises the following steps of: inputting a training sample set and a test image; learning a dictionary and the observation matrix by using a Couple-KSVD (Kernel Singular Value Decomposition) method to obtain a target training dictionary psi and a coupled observation matrix phi; finally reconstructing a small block of a high-resolution image by using a fast Bayesian matching pursuit FBMP algorithm; and repeating the operation on all small blocks of the image to acquire a final SAR high-resolution reconstructed image. By adopting the method, the reconstruction quality of various SAR high-resolution images can be improved at different sampling rates; and the method can be used for recovery and recognition of targets and objects in various SAR images.

Description

technical field [0001] The invention belongs to the technical field of image processing, and relates to a synthetic aperture radar SAR image high-resolution reconstruction method, which can be used for the restoration and identification of targets and objects in various SAR images. Background technique [0002] The size of SAR image is large and the amount of data is high, which brings many problems to the real-time transmission and storage of data. The traditional SAR image compression and transmission process is: collect data under the Nyquist sampling requirement, then compress and code the SAR image, and finally store and transmit the coded value. There are two disadvantages in such a processing method: first, since the sampling rate must not be lower than twice the signal bandwidth, this will bring great pressure to the hardware system; second, in the process of compression encoding, a large number of data, resulting in a waste of data computing and memory resources. ...

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Application Information

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IPC IPC(8): G06T5/50
Inventor 杨淑媛焦李成刘芳周宇万艳艳王晶王爽侯彪缑水平
Owner XIDIAN UNIV
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