Unified feature space image super-resolution reconstruction method based on joint sparse constraint

Abstract

The invention discloses a united feature space image super-resolution reconstruction method based on joint sparse constraint. The feature space image super-resolution reconstruction method based on the joint sparse constraint comprises the achieving steps: (1) taking z images from a natural image base, and constructing a sample set; (2) gathering samples into C types, utilizing joint learning to obtain a low-resolution projection matrix and a high-resolution projection matrix of each type; (3) projecting high-resolution gradient feature samples of each type, and obtaining a sample set Mj; (4) with the joint sparse constraint adopted, carrying out dictionary learning on the Mj and high-resolution details, and obtaining dictionaries of each type; (5) partitioning an input low-resolution image Xt, carrying out projection on an image block with the projection matrixes of each type adopted, obtaining united features of each type, and obtaining a coefficient through the united features and the dictionaries of each type; (6) obtaining reconstruction results with the coefficient and the dictionaries of each type adopted; (7) mixing the reconstruction results through wavelet alternation, and obtaining a high-resolution result rh; (8) repeating from the step (5) to the step (7) to obtain a high-resolution image R0, processing the high-resolution image R0 through use of an iterative back projection (IBP) algorithm, and obtaining a reconstruction result RH. The united feature space image super-resolution reconstruction method has the advantages that the edges of the reconstruction result are clear, and the united feature space 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, and relates to an image super-resolution reconstruction method, which can be used for super-resolution reconstruction of various natural images, and has a better reconstruction effect on image structure information. Background technique [0002] In real life, images have become an important means for people to obtain information, and have been widely used in aerospace and aviation, biomedicine, communications, industrial control, military public security, culture and art, computer vision, video and multimedia systems, scientific visualization, E-commerce and many other fields. In many application fields, such as medical diagnosis, pattern recognition, video surveillance, biometrics, high-definition television HDTV imaging, remote sensing image interpretation, high-altitude earth observation, etc., image processing systems often need to process high-resolution images to improve p...

AI Technical Summary

Problems solved by technology

[0004] (1) The method based on interpolation; this type of method is the most intuitive method in the current super-resolution reconstruction method. The advantage of this type of method is that the algorithm is fast and easy to implement. High resolution results suffer from severe distortion;

[0005] (2) Reconstruction-based methods; this type of method uses some prior knowledge of the image to estimate the details of the high-resolution image, and some scholars introduce some regularization methods to improve the estimation quality of the high-resolution image, such as bilateral aggregate V

Images