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Edge structure information based block compression perception reconstruction method

A block-compressed sensing and edge structure technology, which is applied in the field of image processing, can solve problems such as image edge blurring, and achieve fast speed, accelerated reconstruction speed, and good reconstruction effect

Active Publication Date: 2011-04-27
XIDIAN UNIV
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Problems solved by technology

The main advantage of this is that this method is to measure each small block independently, so it is fast and takes up less memory, but the disadvantage is that although the post-processing removes the block effect, it blurs the edge of the image

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  • Edge structure information based block compression perception reconstruction method
  • Edge structure information based block compression perception reconstruction method

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[0024] refer to figure 1 , the specific implementation process of the present invention is as follows:

[0025] Step 1. Divide an image into several small blocks. The size of the small blocks in this example is 32×32, but it is not limited to this value;

[0026] Step 2, perform wavelet transform on each image block, retain the low-frequency signal, compress and sample the high-frequency signal, and obtain the measurement vector y:

[0027] y=A*x, where x is the wavelet transform of the original image, A is the M×N random perception matrix, where M<<N;

[0028] Step 3, restore the image with low-frequency information, and perform edge detection on the restored image;

[0029] Set all the high-frequency parts of the transformed image block to zero, and perform inverse wavelet transform together with the retained low-frequency information, splicing the inversely transformed image blocks to obtain a blurred image, and use the canny operator on the blurred image Perform edge de...

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Abstract

The invention discloses an edge structure information based block compression perception reconfiguration method which mainly overcomes the defects of low speed and obvious block effect of a reconstructed image in the prior art. The method comprises the following steps of: (1) dividing an image into 32*32 blocks; (2) carrying out wavelet transform on the image blocks, reserving low-frequency signals of the image blocks, and compressing and sampling high-frequency signals; (3) zeroing all high-frequency image block parts, carrying out reverse wavelet transform on the reserved low-frequency information and the zeroed parts to obtain a blurred image and carrying out edge detection on the image; (4) reconstructing image blocks without edges by adopting an orthogonal matching pursuit method, reconstructing image blocks with edges by adopting an MP (Monolithic Processor) method guided by edge structure information, and reconstructing non-edge parts by adopting a generalized inverse method; and (5) splicing reconstructed image blocks to obtain a reconstructed image of an original image. Compared with the prior art, the method has the advantages of high speed and good reconstruction effect and is suitable for reconstructing natural images and SAR (Synthetic Aperture Radar) images.

Description

technical field [0001] The invention belongs to the technical field of image processing, in particular to an image block compression sensing reconstruction method, which can be used to reconstruct natural images and SAR images. Background technique [0002] With the improvement of imaging technology and the improvement of image resolution, the amount of image data is also expanding, which brings huge pressure to image transmission. Therefore, it is necessary to effectively compress and reconstruct images. [0003] American scholars Candés and Donoho formally proposed the concept of compressed sensing in 2006 on the basis of traditional compression and reconstruction methods, such as: DONOHO D.Compressed sensing[J]IEEE Trans.Information Theory, 2006, 52(4): 1289 -1306; CANDES E. Compressive sampling [C] / / Proceedings of the International Congress of Mathematicians. Madrid, Spain: [s.n.], 2006: 1433-1452. Its core idea is to combine the sampling and encoding process in the t...

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Application Information

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IPC IPC(8): G06T11/00G06T9/20
Inventor 刘芳焦李成王爽刘丹侯彪郝红侠戚玉涛尚荣华马文萍蒋文梅
Owner XIDIAN UNIV
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