Nonconvex Compressive Sensing Image Reconstruction Method Based on Image Block Structural Attribute Strategy
A structural attribute and compressed sensing technology, applied in the field of image processing, can solve the problems of premature maturity, slow reconstruction speed, and unsatisfactory image reconstruction effect, and achieve the goal of improving accuracy, reconstruction speed, and reconstruction accuracy. Effect
Active Publication Date: 2017-10-24
XIDIAN UNIV
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In addition, this method uses the traditional genetic algorithm for evolutionary learning, and the process of individual selection is likely to cause premature maturity and fall into local optimum
Therefore, the image reconstruction effect is not ideal, and the reconstruction speed is relatively slow
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[0033] refer to figure 1 , the implementation steps of the present invention are as follows:
[0034] Step 1, perform block observation on the original image.
[0035] Input the original image and divide it into 16×16 non-overlapping blocks; use the random Gaussian observation matrix Φ to observe each block to obtain the measurement vector y, and send the observation matrix Φ and the measurement vector y of each block through the sending end , the receiving end receives.
[0036]In this embodiment, the natural image of 512 * 512 is divided into image blocks of 16 * 16 to obtain 1024 image blocks; all image blocks are stored as column vectors with matlab software in the computer, and the column vectors corresponding to all image blocks are combined with The same Gaussian observation matrix is multiplied to get 1024 observation vectors.
[0037] Step 2, perform local similarity clustering on the observation vectors.
[0038] (2.1) A clustering mark is set for all image blo...
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The invention discloses a non-protruding compressed sensing image reconstructing method based on the image block structure attribute strategy to mainly solve the problems that in the prior art, the reconstruction speed and the accuracy are low. The non-protruding compressed sensing image reconstructing method comprises the steps that firstly, image blocks are observed, and local similarity clustering is carried out on observation vectors; secondly, the structure attributes and the consistency or the structure attributes of the image blocks corresponding to all the types of observation vectors are judged, and the image blocks with the inconsistent attributes are clustered again; thirdly, the smooth image blocks corresponding to each type of observation vector are reconstructed through the optimization genetic algorithm of the front five dimensions of a dictionary, an optimal atom combination in the dictionary direction is obtained for the non-smooth image blocks, optimal atom combining is carried out on the learn dimension and the displacement, and the non-smooth blocks are reconstructed; fourthly, all the reconstructed image blocks are spliced according to the sequence to obtain a complete reconstructed image. The non-protruding compressed sensing image reconstructing method has the advantages that the reconstructing speed is high, the visual effect of a reconstructed image is good, and the peak signal to noise ratio is high.
Description
technical field [0001] The invention belongs to the technical field of image processing, and in particular relates to an image reconstruction method, which can be used for image restoration and recognition classification. Background technique [0002] In recent years, with the rapid development of information technology, people's demand for information is also increasing day by day. Nyquist sampling theory cannot meet the growing demand for information, and it is no longer the optimal sampling theory. The emergence of the compressive sensing theory CS has brought a breakthrough for the new sampling theory. The theory points out that as long as the signal is compressible or sparse in a transform domain, an observation matrix unrelated to the transform basis can be used to project the transformed high-dimensional signal onto a low-dimensional space, and then The original signal is reconstructed with high probability from these few projections. Under this theoretical framewor...
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IPC IPC(8): G06T9/00G06T7/60
Inventor 刘芳王增琴李玲玲焦李成郝红侠林乐平杨淑媛张向荣马晶晶尚荣华
Owner XIDIAN UNIV