A single image super-resolution reconstruction method based on three-channel convolutional neural network

Abstract

The present invention provides a method for super-resolution reconstruction of a single image based on a three-channel convolutional neural network in the field of image processing. The high-resolution image and the low-resolution image corresponding to the high-resolution image; step S20, creating a three-channel convolutional neural network model, and using the three-channel convolutional neural network model for each high-resolution image and low-resolution image Carry out training, and generate the mapping relationship between the low-resolution image and the high-resolution image; step S30, use the mean square error loss function to optimize the mapping relationship; step S40, based on the optimized mapping relationship, to be reconstructed The low-resolution image is input to the three-channel convolutional neural network model, and the reconstructed high-resolution image is output. The advantage of the present invention is that the quality of the reconstructed image is greatly improved without increasing the network depth and model parameters.

Description

technical field [0001] The invention relates to the field of image processing, in particular to a single image super-resolution reconstruction method based on a three-channel convolutional neural network. Background technique [0002] Image is one of the most convenient and fast carriers to transmit information. It has the characteristics of large amount of information, intuition, and can be saved. Therefore, it is widely used in medicine, public security, remote sensing, national defense and other fields. However, due to the limitations of imaging equipment and environmental factors, the quality of the acquired images is reduced, which is not conducive to the accuracy and integrity of information transmission, so how to improve the image quality is very important. Image resolution is an important indicator to measure image quality, which is expressed subjectively as the clarity and richness of image edges and texture details; objectively, it is expressed as the total number...

AI Technical Summary

Problems solved by technology

[0008] Compared with reconstruction-based methods, Markov network-based super-resolution reconstruction methods can obtain rich high-frequency information, and can still reconstruct higher-quality images at higher magnifications, but there is a problem with training samples The selection requirements are high and the shortcomings of being sensitive to noise

Compared with the super-resolution reconstruction method based on the Markov network, the super-resolution reconstruction method based on domain embedding requires fewer training samples and is less sensitive to noise, but has the disadvantage of being difficult to choose the size of the domain

The method based on sparse representation solves the problem of domain size in the super-resolution reconstruction method based on domain embedding, but it has the disadvantage of being difficult to select an over-complete dictionary; randomly selecting an over-complete dictionary can better reconstruct images in a specific domain, but for The reconstruction effect of ordinary images is not ideal

[0009] The super-resolution reconstruction method based on deep learning has the advantages of high reconstruction quality, fast reconstruction speed, and even real-time super-resolution reconstruction. The disadvantage is that the calculation is huge and requires high computer hardware configuration.

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