High-resolution image reconstruction method based on low-rank tensor and hierarchical dictionary learning

Abstract

A high-resolution image reconstruction method based on a low-rank tensor and hierarchical dictionary learning is provided. The method comprises steps of: up-sampling and down-sampling an original image by using bilinear interpolation, and using a processed result and the original image as a dictionary learning training set; training the original image and a down-sampled image, extracting the down-sampled image gradient, arranging the original image and the down-sampled image gradient as the tensor, and subjecting the down-sampled image gradient tensor to low-rank approximation; subjecting the original tensor and the approximate down-sampled gradient tensor to sparse dictionary learning to obtain an image restoration dictionary; training a low-resolution image and the up-sampled image, extracting the low-resolution image gradient, arranging the low-resolution image gradient and up-sampled image as the tensor, learning to update the dictionary; transferring the original image to a YCbCr space, reconstructing the Y with the dictionary, reconstructing the Cb and the Cr by bilinear interpolation to obtain the original restored image; and subjecting the original restored image to global enhancement by using iteration back projection to obtain a final result. The method retains the structure information of the image by using the tensor and improves the precision of image reconstruction.

Description

technical field [0001] The invention relates to the field of high-resolution image reconstruction. In particular, it concerns a method for high-resolution image reconstruction based on low-rank tensor and hierarchical dictionary learning. Background technique [0002] The reflection of the human eye on the objective world through various observation systems is called an image. People perceive shape, size, position, distance, etc. through vision, and make corresponding judgments. The development of society has made people's requirements for obtaining high-resolution digital images more and more prominent. Whether it is for military or civilian use, how to obtain a high-resolution digital image has become a problem that people must solve. [0003] The so-called high-resolution digital image refers to a digital image with dense spatial distribution, that is to say, the image has more pixel sets per unit space. For example, medical CT images can be used as the basis for docto...

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However, although the image processed in this way can increase the number of pixels in the unit space, in essence, it does not break through the info

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