A fast image super-resolution reconstruction method and device based on sample learning

Abstract

The invention discloses a fast image super-resolution reconstruction method and device based on sample learning. The method includes the following steps: step S1, obtaining training samples through multiple high- and low-resolution images containing exactly the same content to perform model Training, to obtain hierarchical clustering trees and regression matrices corresponding to multiple image block sizes; step S2, using the clustering trees and regression matrices corresponding to multiple image blocks obtained through training, to perform adaptive multi-image block segmentation on low-resolution images Local linear regression is used to obtain high-quality reconstructed high-resolution images. Through the present invention, new high-frequency information can be better added to the image, and at the same time, high-resolution images can be reconstructed quickly.

Description

technical field [0001] The invention relates to the fields of digital image processing, machine learning and artificial intelligence, in particular to a fast image super-resolution reconstruction method and device for rapidly enlarging low-resolution images to obtain high-quality and high-resolution images by using sample learning. Background technique [0002] As an important form of information for humans to perceive the world, images are rich and detailed in their content, which directly determines the level of detail that humans perceive. The higher the pixel density on the image unit scale, the clearer the image, the stronger the ability to express details, and the richer the information perceived by humans, which is a high-resolution image. The super-resolution reconstruction of images has been studied in many fields, such as remote sensing images, satellite imaging, medical images, and some high-definition display fields. [0003] A method to improve the resolution o...

AI Technical Summary

Problems solved by technology

These interpolation methods are intuitive and simple, but often lead to blurred details and cannot increase high-frequency information;

[0007] 2. The learning-based super-resolution method can obtain prior knowledge from a large number of training sample sets as the basis for super-resolution, and can generate new high-frequency details, but the speed is too slow

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