Image super-resolution method based on dictionary learning and non-local total variation

Abstract

The invention discloses an image super-resolution method based on dictionary learning and non-local total variation. The image super-resolution method based on the dictionary learning and the non-local total variation mainly solves the problems of ring effect, lost high-frequency information, and inaccuracy of boundary matching of a super-resolution method and the like. The achieving steps include (1), inputting an image training set; (2) using a KSVD algorithm to train two corresponding high-resolution dictionary and a low-resolution dictionary; (3) conducting sparse representation of a low-resolution input image, and determining sparse coefficient; (4) using determined sparse coefficient and the high-resolution input image to obtain a high-resolution image; (5) conducting ring effect removal of the non-local total variation on the reconstructed high-resolution image; (6) conducting high-frequency information enhancing of the high-resolution image by error compensation, and obtaining a final result. By showing of a simulation experiment, compared with the prior art, the image super-resolution method based on the dictionary learning and the non-local total variation has the advantages of being simple in operation, small in noise, clear in edge and the like, and can be used for obtaining a high-definition image.

Description

technical field [0001] The invention belongs to the technical field of image processing, and relates to an image super-resolution method, especially an image super-resolution method based on dictionary learning and non-local total variation, which can be used to improve the resolution of natural images. Background technique [0002] Image resolution is an important indicator of image quality. With the invention of charge-coupled element and complementary metal-oxide-semiconductor image sensor, people have made certain progress in the quality of acquired images, but image sensors are vulnerable to blur, undersampling, noise, etc. factors, so it is difficult to further improve the image quality. People hope to obtain high-resolution images by improving the image sensor from the hardware aspect, but the cost of this method is too expensive and difficult to popularize. Therefore, some people propose to use image super-resolution method to improve the acquired image resolution....

AI Technical Summary

Problems solved by technology

[0005] In the document Image super-resolution as sparse representation of raw image patches, Yang et al. of the University of Illinois in the United States proposed to use dictionary learning and sparse representation theory to realize the image of a single frame image. Super-resolution, in this method, due to the sparse representation over-reliance on the constructed over-complete dictionary and the defects of its dictionary learning algorithm, the edge of the obtained high-resolution image has a ringing effect, and the edge and texture of the high-resolution image are not clear enough or even the real edge Contradictory textures, loss of high-frequency information, etc.

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