Image super-resolution reconstruction method based on attention mechanism and dual-channel network

Abstract

The invention belongs to the field of artificial intelligence, deep learning and image processing, and particularly relates to an image super-resolution reconstruction method based on an attention mechanism and a two-channel network. The method comprises the following steps: obtaining a to-be-detected image in real time, and carrying out the preprocessing of the to-be-detected image; inputting the preprocessed image into the trained image super-resolution reconstruction model to obtain a high-definition reconstruction image; evaluating the reconstructed image by adopting a peak signal-to-noise ratio and structural similarity, and marking the high-definition reconstructed image according to an evaluation result; the basis of the image super-resolution reconstruction model is a convolutional neural network; according to the method, a dual-channel network is used, one network uses an improved residual structure to extract valuable high-frequency features, namely advanced features, and the other network uses an improved VGG network, so that it is ensured that the sizes of input and output images are consistent, rich low-frequency features are extracted, and finally feature fusion is performed, so that the reconstructed image is clearer.

Description

technical field [0001] The invention belongs to the fields of artificial intelligence, deep learning and image processing, and specifically relates to an image super-resolution reconstruction method based on an attention mechanism and a dual-channel network. Background technique [0002] Image super-resolution reconstruction technology uses a set of low-quality, low-resolution images (or motion sequences) to generate high-quality, high-resolution images. Image super-resolution reconstruction is applied in various computer vision tasks, including surveillance imaging, medical imaging, object recognition, etc. In real life, limited by the cost of image acquisition equipment, video image transmission bandwidth, or the technical bottleneck of the imaging modality itself, we are not always able to obtain large-scale high-definition images with sharp edges and no blocky blur . In the context of this need, super-resolution reconstruction techniques emerge as the times require, an...

AI Technical Summary

Problems solved by technology

[0003] In traditional image reconstruction methods, CNN convolutional neural network calculations are often used, and a lot of smooth information will be gradually lost during the calculation process. The main purpose of image super-resolution reconstruction is to obtain high-precision high-definition images, and any loss of information will Affects the final reconstruction accuracy; and the calculation of the CNN convolutional neural network adopts the method of local receptive fields. During the calculation, it is limited by the size of the convolution kernel. The information obtained is the local information of the picture, and the entire picture cannot be obtained. The global information of the image, and the pixels in the picture are related, and the information of the long-distance dependence is also an important dependence information of image reconstruction.

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