Image super-resolution reconstruction method based on self-similarity and structural information constraint

Abstract

The invention discloses an image super-resolution reconstruction method based on self-similarity and structural information constraint. The image super-resolution reconstruction method based on the self-similarity and the structural information constraint comprises the achieving steps: (1) taking z images from an image base, carrying out imitating quality degradation on each image, generating a low-resolution image, and constructing a dictionary training sample set; (2) in the dictionary training sample set, learning a pair of high resolution ratio dictionary and low resolution ratio dictionary through a kernel singular value decomposition (K-SVD) method; (3) for a to-be-processed low-resolution image Xt, with scale rotation transform utilized, searching k similar blocks {p1,p2,...,pk} which are mostly similar with an image block xi; (4) carrying out constraint solution on the image block xi through the obtained k similar blocks to obtain a sparse presentation coefficient A; (5) obtaining k reconstruction results through the sparse presentation coefficient A combined with a high-resolution dictionary DH; (6) utilizing a low rank presentation model, amending a similarity degree of the reconstruction results with the similar blocks {p1,p2,...,pk} under the low resolution utilized; (7) obtaining a final result through the amended similarity degree combined with the reconstruction results; and repeating the steps in sequence and obtaining a final high-resolution image YH. The image super-resolution reconstruction method based on the self-similarity and the structural information constraint has the advantages that structural information of the reconstruction results keeps good, and the image super-resolution reconstruction method can be used for image recognition and target classification.

Description

Technical field [0001] The invention belongs to the technical field of digital image processing, relates to an image super-resolution reconstruction method, and can be used for super-resolution reconstruction of various natural images. Background technique [0002] In the process of image acquisition and transmission, it is often affected by the physical resolution of the imaging system, scene changes and weather conditions, and many other factors, which reduce the resolution of the original image, which not only affects human subjective visual effects, but also seriously It hinders the subsequent target classification and recognition work. Therefore, image super-resolution reconstruction has become an indispensable key step. This technology can restore the original image, improve image quality, and highlight the characteristics of the image itself, thereby laying a good foundation for subsequent image processing, analysis and understanding. [0003] At present, image super-resolu...

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Problems solved by technology

[0004] The purpose of the present invention is to address the deficiencies of the above-mentioned prior art, and propose an image super-resolution reconstruction method based on self-similarity and structural information constraints, to reduce the influence of reconstruction errors in the process of image super-resolution, and effectively solve the problem of image reconstruction. The problem of loss of details in the medium and ringing at the edge of the image can better restore the structural information of the image and improve the reconstruction effect of the image

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