An image super-resolution reconstruction method of a generative adversarial network based on Gaussian coding feedback

Abstract

The invention discloses an image super-resolution reconstruction method of a generative adversarial network based on Gaussian coding feedback, and the method comprises the steps: carrying out the preprocessing of an ImageNet data set, and manufacturing a reconstruction data set in one-to-one correspondence with a low-resolution image and a high-resolution image; Constructing a generative adversarial network model for training, and introducing a Gaussian coding feedback network into the model; Sequentially inputting the data sets obtained in the step A into a generative adversarial network formodel training; And inputting the low-resolution image to be processed into a trained generative adversarial network to obtain a high-resolution image. A generative adversarial network is formed by constructing a generation network and a discrimination network, a Gaussian coding feedback loop is added between the generation network and the discrimination network, more information is added to the generation network to guide the generation network to carry out training, and important features are added by improving a sub-pixel convolutional layer structure, so that useless information is reduced, and the reconstruction effect is improved.

Description

technical field [0001] The present invention relates to the field of image reconstruction methods, in particular to an image reconstruction method based on Gaussian coding feedback generation confrontation network. Background technique [0002] Super-resolution reconstruction (SR) is a technique for recovering a corresponding high-resolution image from a given low-resolution image. With the development of science and technology, people's demand for high-resolution images and videos is gradually increasing, but limited by the acquisition equipment and environment, the resolution of the collected images is low and cannot be used for practical applications. Due to the urgent need for high-resolution images in many applications and fields and the high cost of changing hardware systems, the use of algorithms to improve image resolution has become a research hotspot. In recent years, super-resolution reconstruction technology has been widely researched and applied in medical imag...

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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