Compressive-sensing-based object reconstruction method

A compressed sensing and target technology, applied in the field of image processing, can solve the problems of inability to find the target of interest in the scene, no prominent scene target, heavy burden on hardware equipment, etc., to save resources and costs, save time, and reduce the burden.

Inactive Publication Date: 2012-07-18
XIDIAN UNIV
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Problems solved by technology

[0004] 1) Since there is no prior knowledge of the target in the reconstruction process, these methods can only be used for the reconstruction of the entire scene, without the function of highlighting the target in the scene, and unable to find the target of interest in the scene. The entire image constructed can be processed to determine the position of the target;
[0005] 2) There is a high requirement on the sampling rate, so it brings a great burden to the sampling hardware equipment

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  • Compressive-sensing-based object reconstruction method
  • Compressive-sensing-based object reconstruction method
  • Compressive-sensing-based object reconstruction method

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Embodiment Construction

[0028] refer to figure 1 , the specific implementation steps of the present invention are as follows:

[0029] Step 1. Perform Gaussian mixture modeling on the target through the mixed factor analysis model to obtain the probability density of the target:

[0030] (1a) Take n images of the target at different angles, use these images as training images, and use the nearest neighbor interpolation method to unify the training images into a size of 32×32 pixels, 1000≤n≤1600;

[0031] (1b) Through the Beta process, the rank J of the covariance matrix of the following Gaussian distribution is obtained:

[0032] x i ~N(Aw i +μ,α -1 I N );

[0033] where: x i For the training sample dimension is N, A in the mixed factor analysis model represents a set of matrices with a basis of N×J, stretching into a linear subspace, w i is the coefficient of the linear subspace spanned by A in the mixed factor analysis model, its dimension is J, μ is the mean dimension is N, I N is the uni...

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Abstract

The invention discloses a compressive-sensing-based object reconstruction method. The main problem of incapability of detecting an object in image reconstruction in the prior art is solved. The method is implemented by the following steps of: 1) performing hybrid Gaussian modeling on the object to obtain the probability density of the object by using a mixed factor analysis model; 2) blocking the whole image to be reconstructed; 3) reconstructing each image block by utilizing the learnt probability density of the object as the priori knowledge of the object; and 4) splicing the reconstructed image blocks to recover an original integral image to obtain the whole reconstructed image. The object detection and image reconstruction are realized at the same time, so that the method has the advantages of resource saving and high efficiency, and can be used for object detection.

Description

technical field [0001] The invention belongs to the technical field of image processing and relates to the reconstruction of natural images, specifically a compression sensing target reconstruction method adding target prior knowledge, which can be used for target detection. Background technique [0002] Compressed sensing (Compressive Sensing) is a new direction between mathematics and information science, proposed by Candes, Terres Tao, etc., to challenge the traditional sampling coding technology, that is, the Nyquist sampling theorem. Compressed sensing theory has brought a revolutionary breakthrough to signal acquisition technology. It uses non-adaptive linear projection to maintain the original structure of the signal, samples the signal at a frequency much lower than Nyquist, and accurately reconstructs the signal through numerical optimization problems. Construct the original signal. Rice University in the United States has designed a single-pixel camera based on th...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T5/00
Inventor 侯彪焦李成程曦王爽张向荣马文萍
Owner XIDIAN UNIV
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