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

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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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 representation dictionary. When the magnification factor increases ( 4 times and above), the traditional method is difficult to reconstruct details such as clear image edges, and the traditional online learning method is very time-consuming, which is not conducive to the practicality of the algorithm.

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