Image super-resolution reconstruction method based on sparse representation and adaptive filtering

A technology of super-resolution reconstruction and adaptive filtering, applied in image analysis, image enhancement, image data processing, etc., can solve the problems of difficulty in reconstructing clear image edges, limited dictionary generalization ability, and unfavorable algorithm practicality. Avoid the effect of low adaptability of sparse representation dictionary, improve adaptability and strong adaptability

Active Publication Date: 2016-03-16
陕西令一盾信息技术有限公司
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Problems solved by technology

[0006] In order to avoid the deficiencies of the prior art, the present invention proposes an image super-resolution reconstruction method based on sparse representation and adaptive filtering, which can overcome the limited generalization ability of the traditional learning-based sparse represe

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  • Image super-resolution reconstruction method based on sparse representation and adaptive filtering
  • Image super-resolution reconstruction method based on sparse representation and adaptive filtering
  • Image super-resolution reconstruction method based on sparse representation and adaptive filtering

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[0025] The present invention will now be further described in conjunction with examples:

[0026] On a large number of offline high-resolution and low-resolution training image sets, combining K-means clustering and principal component analysis two data processing methods, offline obtain a sparse representation dictionary set with strong adaptability, and then obtain adaptation through further regression analysis The mapping relationship between high-resolution and low-resolution images is used, and finally the mapping relationship obtained offline is used to solve the problem of high-resolution reconstruction of online images. The specific implementation process is as follows:

[0027] 1. Construct a set of high and low resolution image block pairs.

[0028] Choose a Gaussian kernel with a variance of σ of 1 and a size of k×k, and perform Gaussian convolution on each image in the training image set containing 150 high-definition (spatial resolutions are higher than 1024×720, and th...

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Abstract

The invention relates to an image super-resolution reconstruction method based on sparse representation and adaptive filtering. A lot of images are fully clustered by utilizing structure information of image contents firstly, each kind of image set is guaranteed to contain high-consistency image structure information, sparse representation dictionaries of categories are obtained through principal component analyses, performed on the basis, by the categories, and the adaptability is high. Through adoption of a packet minimum angle regression method, an Euclidean projection method on an l<1)-ball, and through a cross iteration mode, high- and low-resolution image block mapping relationship matrixes of each category are solved. Field processing is performed on the low-resolution images directly by utilizing the mapping relationship matrix learned through training finally, and high-definition high-resolution images are rapidly reconstructed.

Description

technical field [0001] The invention belongs to a visible light image processing method, and relates to an image super-resolution reconstruction method based on sparse representation and adaptive filtering. Background technique [0002] The image super-resolution reconstruction technology first appeared in the 1960s. At that time, scholars proposed to apply the method of band-limited signal extrapolation to the super-resolution reconstruction of optical images, which laid the foundation for the existence of super-resolution reconstruction. mathematical basis. It was not until the late 1980s that people made a breakthrough in the research of image super-resolution reconstruction methods, which not only explained the possibility of super-resolution reconstruction in theory, but also proposed many practical methods in practice. . At present, super-resolution reconstruction can be roughly divided into two directions: reconstruction-based methods and learning-based methods. ...

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Application Information

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IPC IPC(8): G06T3/40G06T5/00G06T5/50
CPCG06T3/4053G06T5/00G06T5/50G06T2207/20004G06T2207/20081
Inventor 李映胡杰刘韬
Owner 陕西令一盾信息技术有限公司
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