Low-rank sparse neighborhood embedding method for super-resolution based on normlv features

Abstract

The invention discloses a low-rank sparse neighborhood embedding super-resolution method based on NormLV features. First, the NormLV feature is constructed to enhance the feature of the neighborhood embedding algorithm, and the neighbors are selected through the feature; the training set is grouped to obtain the index set; the weight matrix is ​​calculated by using the low-rank sparse neighborhood embedding algorithm; the weight matrix is ​​normalized ; linear combination to obtain high-resolution image blocks; fusion of high-resolution image blocks to obtain the initial high-resolution image; finally, combined with consistency prior and global constraints, TV and IBP algorithms are used to further improve the quality of image super-resolution reconstruction. The invention combines the sparse representation with the neighborhood embedding algorithm, and solves the technical problem that the inconsistency of the neighborhood relationship between the low-resolution image and the high-resolution image affects the super-resolution reconstruction quality. Using the invention to obtain high-resolution images can restore clearer and richer texture details and image edges, and has better visual effects than other methods.

Description

technical field [0001] The invention belongs to the technical field of image processing, and mainly relates to an image super-resolution method, specifically a low-rank sparse neighborhood embedding super-resolution method based on NormLV features. The high-quality images thus obtained provide a basis for subsequent image processing, analysis and understanding. Great help, can be used in different fields, such as biomedicine, video and multimedia systems, military reconnaissance and other fields. Background technique [0002] With the increasing popularity of Internet applications and the rapid development of mobile communication technology, image super-resolution reconstruction has been widely used in the field of image processing, which can overcome the lack of resolution of imaging systems. It has achieved good results in different fields, such as biomedicine, video and multimedia systems, military reconnaissance and other fields. After nearly 30 years of development and...

AI Technical Summary

Problems solved by technology

However, the traditional super-resolution method based on the neighborhood embedding algorithm has the following problems: 1) The first-order gradient and second-order gradient feature extraction methods can only represent features in the horizontal and vertical directions, and cannot better represent image blocks, which will cause The filtered neighborhood is inaccurate; 2) Due to the inaccuracy of feature extraction, the mapping between low-resolution image blocks and high-resolution image blocks is not a one-to-one linear mapping; 3) Euclidean distance is used to strictly find fixed Linear embedding of k-nearest neighbors can easily lead to under-fitting or over-fitting, and noise samples or external interference samples cannot be excluded when finding the nearest neighbors; , when the size of the training set is large, the algorithm is time-consuming

In summary, the traditional neighborhood embedding super-resolution method is inaccurate in the neighborhood selected by feature representation, searching for k-nearest neighbors cannot exclude noise samples or external interference samples, and calculating reconstruction weights can easily lead to underfitting or overfitting and The algorithm is time-consuming and has some shortcomings

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