Self-learning image super-resolution reconstruction method and system based on convolutional neural network

Abstract

The invention discloses a self-learning image super-resolution reconstruction method and system based on a convolutional neural network. The method comprises the steps of 1, obtaining a training sample of a to-be-reconstructed image; 2, constructing a convolutional neural network; the convolutional neural network comprises a feature extraction unit, a feature enhancement unit, a residual error unit and a reconstruction unit. 3, training the convolutional neural network constructed in the step 2 based on the training sample obtained in the step 1 to obtain a trained reconstructed convolutionalneural network; and 4, performing super-resolution reconstruction on a to-be-reconstructed image based on the reconstructed convolutional neural network trained in the step 3. According to the method,the problem of insufficient training samples of the self-learning algorithm can be effectively solved, and the over-fitting phenomenon of the network can be avoided; meanwhile, a high-resolution image with a higher peak signal-to-noise ratio and a better visual effect can be obtained.

Description

technical field [0001] The invention belongs to the technical field of image processing, in particular to a self-learning image super-resolution reconstruction method and system based on a convolutional neural network. Background technique [0002] With the rapid development of social intelligence and informatization, images have become an important way for human beings to obtain information. They have very important application values ​​in the fields of monitoring equipment, satellite image remote sensing, video restoration, and medical imaging. Due to its high pixel density, high-resolution images can provide more important details for digital image processing. However, due to the limitations of imaging equipment, lighting and other conditions, the resolution of acquired images is often relatively low. How to effectively improve the quality of imaging images has become a very critical and important task in image processing. Image super-resolution reconstruction technology...

AI Technical Summary

Problems solved by technology

The present invention can not only effectively solve the problem of insufficient training samples of the self-learning algorithm, but also avoid the phenomenon of over-fitting in the network; at the same time, it can obtain high-resolution images with higher peak signal-to-noise ratio and better visual effects

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