Method for reconstructing partitioned images by compressive sensing on the basis of structural dictionaries

An image compression and dictionary technology, applied in the field of image processing, can solve the problems affecting the final reconstruction effect of the image, and achieve the effect of overcoming the unsatisfactory reconstruction effect and improving the reconstruction quality.

Active Publication Date: 2012-10-03
XIDIAN UNIV
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Problems solved by technology

[0005] The purpose of the present invention is to solve the shortcomings of using a single dictionary in the existing compressed sensing reconstruction technology, it is difficult to express the image blocks with different str

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  • Method for reconstructing partitioned images by compressive sensing on the basis of structural dictionaries
  • Method for reconstructing partitioned images by compressive sensing on the basis of structural dictionaries
  • Method for reconstructing partitioned images by compressive sensing on the basis of structural dictionaries

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Embodiment Construction

[0052] The present invention will be further described below in conjunction with the accompanying drawings.

[0053] Refer to attached figure 1 , the concrete steps of the present invention are as follows:

[0054] Step 1, get training samples

[0055] The first step is to select multiple original images with various structural feature information such as smooth features, irregular features, and different pointing information from the database, and down-sample these images respectively to obtain down-sampled images.

[0056] In the second step, multiple original images and original images selected in the database are used to form a training sample library.

[0057] In the third step, the images in the sample library are divided into non-overlapping successive blocks according to the size of 8×8, and 20,000 training image blocks are randomly selected, and they are pulled into column vectors sequentially by column as training samples.

[0058] Step 2, image patch classificati...

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Abstract

The invention discloses a method for reconstructing partitioned images by compressive sensing on the basis of structural dictionaries, aiming to overcome the defects that the effect of reconstructing the partitioned images by compressive sensing is not ideal in the prior art because the image blocks with different structures can not be expressed most sparsely on the basis of the signal dictionaries. The method comprises the following steps: (1) constructing a training sample library; (2) classifying image blocks; (3) training the structural dictionaries; (4) constructing an observation matrix; (5) observing the image blocks; (6) reconstructing the structural dictionaries; (7) acquiring reconstruction errors; (8) reconstructing images; and (9) outputting the reconstructed images. The images are reconstructed on the basis of the image blocks under all the structural dictionaries by adopting the error weighting and summing method, and the reconstruction quality is improved.

Description

technical field [0001] The invention belongs to the technical field of image processing, and further relates to a structural dictionary-based block compressed sensing image reconstruction method under the theoretical framework of compressed sensing, which can be used for high-quality reconstruction of various natural images under compressed observation. Background technique [0002] In order to obtain high-resolution images, the number of sensors needs to be increased in traditional image acquisition methods, which will increase the cost and volume of imaging equipment. Compressed sensing theory is a new information acquisition and processing method developed in the field of signal processing in recent years. It samples and compresses sparse or compressible signals at the same time, and can be accurately reconstructed at the terminal using low-resolution sensing devices. signal, thereby solving the bottleneck problem of sampling rate requirements in traditional Nyquist sampl...

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Application Information

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IPC IPC(8): G06T11/00
Inventor 杨淑媛焦李成陈义光刘芳侯彪王爽马文萍齐智峰谢冬梅
Owner XIDIAN UNIV
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