Image super-resolution reconstruction method based on dictionary learning and structure clustering

Abstract

The invention discloses an image super-resolution reconstruction method based on dictionary learning and structure clustering, mainly solving the problem that a reconstructed image based on the prior art has a fuzzy surface and a serious marginal sawtooth phenomenon. The image super-resolution reconstruction method comprises the following implementation steps of: (1) acquiring training samples; (2) structurally clustering the training samples; (3) training by using OMP (Orthogonal Matching Pursuit) and K-SVD (K-Singular Value Decomposition) methods to obtain various dictionaries; (4) working out a sparse expression coefficient of an input low-resolution image block; (5) reestablishing a high-resolution image block by using a high-resolution dictionary and the spare coefficient; (6) performing weighting and summing on the high-resolution image block to obtain the high-resoluiton image block subjected to weighting and summing; (7) obtaining a high-resolution image according to the high-resolution image block; and (8) carrying out high-frequency information enhancement on the high-resolution image through error compensation to obtain a final result. A simulation experiment shows that the image super-resolution reconstruction method has the advantages of clear image surface and sharpened margin and can be used for image identification and target classification.

Description

technical field [0001] The invention belongs to the technical field of 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 certain inhibitory effect on small noises. Background technique [0002] In practical applications, due to the limitations of the physical resolution of the imaging system, as well as the influence of many factors such as scene changes and weather conditions, there are often degradation factors such as optical and motion blur, undersampling, and noise in the actual imaging process, resulting in imaging systems that can only get Images or image sequences with poor quality and low resolution usually cannot meet the requirements of practical applications, which brings many difficulties to subsequent image processing, analysis and understanding, and is not conducive to people's correct understanding of the objective world and its laws. [...

AI Technical Summary

Problems solved by technology

[0006] The image super-resolution method based on learning is an image resolution method first proposed by Freeman et al. in recent years. Its content is to learn the difference between low-resolution images and high-resolution images through Markov random fields and prior knowledge. relationship, and then reconstruct the high-resolution image, but this method cannot well maintain the high-frequency details of the high-resolution image, and the computational complexity is large, and the efficiency is low

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