Perception-oriented image super-resolution reconstruction method and system with large receptive field

Abstract

The invention provides a perception-oriented image super-resolution reconstruction method and system with a large receptive field, and relates to the field of image processing.The method comprises the steps that firstly, original data in a super-resolution reconstruction data set is preprocessed, and paired LR-HR training data is constructed; secondly, inputting the preprocessed data set into a PSNR-oriented super-resolution reconstruction network with a receptive field, and only adopting L1 as training loss for training; then, the trained PSNR-oriented model is used as initialization of a generator, and the discriminator and the generator are alternately trained to obtain a final super-resolution reconstruction model; and finally, loading the model, and inputting a picture needing super-resolution into the trained super-resolution reconstruction network model to obtain a high-resolution image corresponding to the low-resolution image. According to the method, the multi-scale information of the image is effectively extracted, more high-frequency information, complex texture details and the like can be reconstructed, and the image perception index is remarkably improved.

Description

technical field [0001] The invention relates to the technical field of computer vision and the field of image processing, in particular to a perception-oriented image super-resolution reconstruction method and system with a large receptive field. Background technique [0002] Image super-resolution reconstruction technology refers to restoring a given low-resolution image into a corresponding high-resolution image through a special algorithm. Image resolution is a set of performance parameters used to evaluate the richness of detailed information contained in images. High-resolution images tend to contain richer texture details and have higher reliability. It has a wide range of applications in real life, such as super-resolution reconstruction of medical images, which can better restore clear medical images, so as to achieve accurate detection of diseased cells. However, affected by many factors such as the acquisition equipment, the environment, and the image degradation ...

AI Technical Summary

Problems solved by technology

[0005] In view of the above-mentioned problems in the prior art, the present invention proposes a perception-oriented image super-resolution reconstruction method with a large receptive field, which can restore more high-frequency information such as hair and t

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