Single-image super-resolution method based on multi-scale structural self-similarity and compressive sensing

Abstract

A single-image super-resolution method based on the multi-scale structural self-similarity and the compressive sensing comprises the following steps of: firstly setting an initial estimated value of a high resolution reconstructed image, setting a stopping error and the maximum time of iteration, determining a downsampling matrix and a fuzzy matrix according to the process of image degradation to construct an image pyramid, and building a dictionary by using the image pyramid as a training sample of the K-SVD (K-singular value decomposition) method; secondly, according to a Nonlocal method, searching for similar image blocks with the same scale in the current high resolution reconstructed image and determining a weight matrix; thirdly, updating the estimated value of the high resolution reconstructed matrix, updating the sparse representation coefficient, and updating the estimated value of the high resolution reconstructed matrix again; and fourthly carrying out the next iteration until two sequential high resolution reconstructed matrixes meet the corresponding requirement or reach the maximum time of iteration. The single-image super-resolution method of the invention adds the additional information contained in a multi-scale self-similar structure of an image into the high resolution reconstructed image through a compressive sensing frame, thereby having a high computational efficiency.

Description

technical field [0001] The invention relates to a single image super-resolution method based on multi-scale structural self-similarity and compressed sensing. Background technique [0002] High-resolution images can provide a lot of detailed information, so the acquisition of high-resolution images is of great significance in many fields. Image resolution is limited by various factors such as imaging platform, imaging equipment manufacturing process, and cost. Therefore, in practical applications, super-resolution methods are usually used to improve the spatial resolution of images. Super-resolution methods use signal processing methods to reconstruct high-resolution images from single or multiple low-resolution images. Traditional super-resolution methods usually use multiple low-resolution images, and use their complementary information to reconstruct high-resolution images. This makes it an urgent problem to be solved in the current super-resolution technology to improv...

AI Technical Summary

Problems solved by technology

Since the samples used for dictionary learning are taken from the image library, it will bring two problems: First, due to the variety of image content, in order to make all image blocks have a better sparse representation under the trained dictionary, The image library used to build the dictionary must have a large scale, which makes it difficult for the dictionary learning process to converge; in addition, the image library may not be able to provide the additional information required for low-resolution images to be processed, although for training samples The dictionary is optimal, but this global dictionary is neither optimal nor efficient for a particular image patch

However, most of the current super-resolution methods based on structural self-similarity only use self-similar structures of the same scale, but do not use self-similar structures of different scales, so the acquisition of additional information is limited; Search for similar image blocks in the entire image, so the computational complexity is high

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