A method for image super-resolution reconstruction

Abstract

The invention discloses a method for image super-resolution reconstruction. The steps include: processing a high-resolution image to obtain an interpolation image of a low-resolution image, and then training to obtain a high- and low-resolution dictionary pair; inputting a low-resolution image, performing Interpolation processing to obtain an interpolated image of a low-resolution image; decompose the low-resolution interpolated image into low-resolution structural parts and texture parts, and discard the low-resolution texture part; extract features from the low-resolution interpolated image, Obtain the low-resolution image features; according to the high- and low-resolution dictionary pairs, perform sparse reconstruction on the low-resolution image features to obtain the high-resolution image texture part; merge the high-resolution image texture part with the low-resolution structure part to obtain the reconstruction High-resolution images after construction. The present invention can classify and train corresponding samples, and then use the corresponding dictionary pairs for subsequent super-resolution reconstruction according to the classification training, and can more accurately improve the resolution of the reconstructed image.

Description

technical field [0001] The invention relates to the field of image processing, in particular to an image super-resolution reconstruction method. Background technique [0002] With the advancement of modern science and technology, digital images have been used more and more widely, and have gradually evolved into one of the most important information carriers. The resolution of an image is an objective standard for evaluating the content richness of an image. The higher the resolution, the richer the content of the image, and the more information people can analyze from it. However, in reality, there are many reasons why the acquired image resolution cannot meet the requirements, such as hardware, which is affected by the sensor array density limitation; external conditions, atmospheric flow, changes in lighting conditions, and relative motion of objects, etc. will also cause The captured image is blurry and the resolution is too low. Therefore, exploring ways to improve im...

AI Technical Summary

Problems solved by technology

However, in Yang's method, the high-resolution image blocks and low-resolution image feature matrices collected from the sample library are directly used as training samples for over-complete dictionary pair training, ignoring some prior information of the sample library, and at the same time The result of subtracting the mean value of the low-resolution interpolated image from the low-resolution interpolated image is also used as a resource to reconstruct the corresponding high-resolution part of the target, and then the mean part of the low-resolution interpolated image is added to obtain the final high-resolution image

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