Image super-resolution reconstruction method for generating antagonistic network based on feature fusion

Abstract

The invention discloses an image super-resolution reconstruction method for generating antagonistic network based on feature fusion, which comprises the following steps: pre-processing ImageNet data set to obtain a reconstruction data set corresponding to high-resolution image and low-resolution image; The model of generating antagonistic network for training is constructed, and the interpolationreconstruction module is introduced into the model to construct the generating network and perceptual network for multi-feature fusion. The reconstructed data sets are sequentially input into the generated antagonistic network for model training. The images to be processed are normalized to obtain low-resolution images, which are input to the trained generation network to obtain the reconstructedhigh-resolution images. As that recursive residual network is utilize to extract multiple features such as edge and texture, highlight the edge and texture information of the generated image, so thatthat reconstruct image is clearer, The interpolation reconstruction module is used as a mediator to realize the residual discrimination, highlight the difference between the reconstructed and real high-resolution images and reduce the computational complexity of the discrimination network, and use VGG network to fuse multiple features to calculate the loss function, which makes the reconstructed image more effective and more consistent with the observation mode of human eyes.

Description

technical field [0001] The present invention relates to the field of image reconstruction methods, in particular to an image reconstruction method based on a feature fusion generating confrontation network. Background technique [0002] Super-resolution reconstruction (SR) is a technique for recovering corresponding high-resolution images from low-resolution images. The acquisition and processing of digital images will inevitably weaken the resolution of images, thus affecting the application of multimedia. Therefore, how to convert low-quality, limited-resolution images into high-quality, high-resolution images has always been a hot research issue involving social production and national defense and military fields. [0003] At present, super-resolution reconstruction algorithms can be mainly divided into three categories: methods based on interpolation reconstruction, methods based on reconstruction and methods based on learning. The method based on interpolation reconst...

AI Technical Summary

Problems solved by technology

The main problem is that the edge details of the reconstructed image are insufficient, and the performance of the final image is not good.

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