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Image sparse representation method based on Curvelet redundant dictionary

A redundant dictionary and sparse representation technology, applied in the field of image processing, can solve the problems of large search effective atom space, large scale of redundant dictionary, huge calculation amount, etc., to improve quality and visual effect, short running time and complex calculation low degree of effect

Inactive Publication Date: 2011-04-13
XIDIAN UNIV
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Problems solved by technology

[0005] (1) The size of the redundant dictionary is large, which leads to a sparse image and a large space for searching effective atoms, which increases the search time complexity, resulting in a huge amount of calculation
[0006] (2) Existing redundant dictionaries cannot effectively sparsely represent rich image edge contour details, such as straight lines and curves.

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  • Image sparse representation method based on Curvelet redundant dictionary
  • Image sparse representation method based on Curvelet redundant dictionary
  • Image sparse representation method based on Curvelet redundant dictionary

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Embodiment Construction

[0029] refer to figure 1 , the specific implementation process of the present invention is as follows:

[0030] Step 1, choose the Curvelet tight frame as the atomic model.

[0031] The Curvelet tight frame is a Curvelet function characterized by a triplet (j, θ, k) to represent, where j is the scale parameter, θ is the direction parameter and k is the displacement parameter, namely

[0032]

[0033] Among them, D a is the scaling operator, a=2 -2j ; θ is the rotation operator of θ angle, displacement parameter k 1 , k 2 Both are integers; (x, y) is the coordinate value of the pixel in the image; The function is a wavelet mother function: where t is a free variable;

[0034] scale operator a=2 -2j , the rotation operator of θ angle Substituting into the Curvelet tight framework, we can get

[0035]

[0036] Let X=2 2j xcos theta-2 2j ysinθ-k1, Y=2 j xsinθ+2 j ycosθ-k2, the Curvelet function is obtained in the form of dot product The simplified...

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Abstract

The invention discloses an image sparse representation method based on a Curvelet redundant dictionary, mainly aiming to solve the problems that in the existing method, the redundant dictionary has large scale, the calculation complexity is high, and sparse representation can not be effectively carried out on the rich border outline details in the image. The invention is realized through the following steps: (1) selecting the tight frame of Curvelet as an atomic model; (2) determining the numeric areas of the scale parameter j, direction parameter theta and displacement parameter k in the frame, carrying out discretization on each parameter to form the Curvelet redundant dictionary; and (3) blocking each input image, carrying out sparse decomposition on each sub-image by utilizing an orthogonal matching pursuit (OMP) algorithm sparse decomposition to solve sparse coefficient vectors, combining all the sparse coefficient vectors to obtain the sparse matrix, and multiplying the sparse matrix by the Curvelet redundant dictionary to obtain the sparse representation results of the input image. Compared with the prior art, the invention has the advantages of low calculation complexity, high quality of sparse representation image, especially can better capture the singularity of curves in the image, and can be applied to the fields of image processing and computer vision.

Description

technical field [0001] The invention belongs to the technical field of image processing, relates to a method for constructing a redundant dictionary and a method for sparsely representing an image with the dictionary, and can be applied to image processing and computer vision. Background technique [0002] In many signal processing applications, people hope to find a sparse data representation and replace the original data representation with sparse approximation, so as to substantially reduce the cost of signal processing and improve compression efficiency. Sparse representation is one of the key technologies in the field of image processing and computer vision. It requires that the absolute value of the coefficients of most of the basis functions in the linear expansion of the image be close to zero, and only a few basis functions have large non-zero coefficients, and Limited large coefficients can represent most of the information of the image. [0003] Traditional spars...

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Application Information

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IPC IPC(8): G06T9/00
Inventor 刘芳焦李成王爽黄婉玲侯彪郝红侠戚玉涛尚荣华马文萍马红梅
Owner XIDIAN UNIV
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