Image super-resolution method based on high and low frequency semaphore

Abstract

The invention relates to an image processing technology, in particular to an image super-resolution method based on high and low frequency semaphore, which comprises the following steps: S1, acquiringa first image data set; S2, performing image preprocessing on the first image data set to obtain a second image data set; S3, building a network model; S4, inputting the second image data set into the network model, and performing prediction by using a feed-forward network to obtain a third image data set; S5, performing weight analysis on the first image data set and the third image data set toobtain a prediction model; and S6, inputting the low-resolution image to be measured into the prediction model to generate a high-resolution prediction image. The method has the beneficial effects that super-resolution reconstruction is performed on the image by establishing the network model, and the processing speed can be improved by separately processing high-frequency and low-frequency features in the image, so that a clearer high-resolution image is obtained, and the problem that the speed and the effect cannot be achieved at the same time in the prior art is solved.

Description

technical field [0001] The invention relates to the technical field of image processing, in particular to an image super-resolution method based on high and low frequency signal quantities. Background technique [0002] Super-resolution (Super-Resolution) is to improve the resolution of the original image through hardware or software. The process of obtaining a high-resolution image through a series of low-resolution images is super-resolution reconstruction. The core idea of ​​super-resolution reconstruction is to use time bandwidth to obtain multi-frame image sequences of the same scene in exchange for spatial resolution, and realize the conversion from temporal resolution to spatial resolution. [0003] Existing technologies are divided into traditional image processing algorithms and deep learning. Existing traditional image processing algorithms, such as Gaussian filtering and bilinear interpolation mentioned in "A Medical Image Processing Device and Image Processing Me...

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Problems solved by technology

[0003] The existing technology is divided into two schools of traditional image processing algorithms and deep learning. The existing traditional image processing algorithms, such as Gaussian filtering and bilinear interpolation mentioned in "A Medical Image Processing Device and Image Processing Method", are faster , but the generated image is relatively smooth, with a feeling of oil painting, which makes people feel unreal; the existing super-resolution technology in the direction of deep learning is better than the traditional method, but the processing takes a long time, and the image in the image High and low frequency information are treated equally, which limits the effect of super-resolution reconstruction to a certain extent

[0004] In practical applications, due to the limitation of scanning technology, the image is not clear and blurred easily caused by the out-of-focus scanning lens, so a fast, higher PSNR (Peak Signal to Noise Ratio, peak signal-to-noise ratio) and SSIM (structural Similarity index, structural similarity) The super-resolution method of the two indicators for evaluating image quality has extremely important significance in actual clinical practice, and provides auxiliary help for doctors to diagnose various diseases

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