Super-resolution image reconstruction method based on sparse multi-manifold embedding

Abstract

The invention discloses a super-resolution image reconstruction method based on sparse multi-manifold embedment. The super-resolution image reconstruction method based on sparse multi-manifold embedment comprises the steps that medium-frequency and high-frequency characteristics of a set of high-resolution training images are extracted to build a medium-frequency and high-frequency characteristic training library; clustering is carried out on the medium-frequency and high-frequency characteristic training library on the basis of the multi-manifold hypothesis, and medium-frequency and high-frequency characteristic set pairs of different classifications are obtained; medium-frequency characteristics of an input low-resolution image through the method same as the method for extracting medium-frequency characteristics of the training images, the nearest medium-frequency characteristic training center of the medium-frequency characteristics is found out, and the classification of the medium-frequency characteristic training center is appointed as a neighborhood search range of the low-resolution image; the positions of sparse neighbors, from the same manifold, of each processed medium-frequency block in the classification are determined by solving a sparse optimization problem, reconstructed high-frequency blocks are obtained through the least square solution, and after processing of all the blocks is accomplished, a high-frequency image can be formed in a composite mode; the high-frequency image is added to the amplified low-resolution image, and an initially-estimated reconstructed image is obtained; the initially-estimated reconstructed image is processed through a common post-processing method, so that the final result is obtained.

Description

technical field [0001] Aiming at the problem of image super-resolution reconstruction in image processing technology, the present invention proposes a super-resolution image reconstruction method based on sparse multi-manifold embedding based on the traditional neighborhood embedding method. The new feature matching and sparse solution are The appropriate number of neighbors is obtained for different image blocks, and finally the reconstruction result is obtained by embedding. This method can be used for super-resolution reconstruction of various natural images. Background technique [0002] With the emergence and use of a large number of images, high-resolution images are more and more popular in many practical applications. Due to the limitations of imaging environment, equipment and cost, image super-resolution reconstruction methods have attracted the attention of many researchers. This technology breaks through the resolution limit of the image sensor, and reconstructs...

AI Technical Summary

Problems solved by technology

However, there are still some deficiencies in this type of method, mainly including two problems: the extracted features cannot effectively represent the image characteristics, thus affecting the reconstruction quality of the image; a large number of image blocks may exist on multiple manifolds and be located on the same Image blocks at different positions in a manifold should have different numbers of neighbor embeddings, so the manifold assumptions of previous neighbor embeddings do not satisfy

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