Dense connection generative adversarial network single image super-resolution reconstruction method

Abstract

The invention belongs to the field of video and image processing. The objective of the invention is to further improve the reconstruction effect and reconstruction precision of a high-resolution image, promote the structure of the generative adversarial network and the improvement of a loss function; the invention discloses a dense connection generative adversarial network single image super-resolution reconstruction method. A generation network and an adversarial network are included. A basic framework of a residual dense network RDN is adopted by the generation network; the adversarial network adopts a deep convolution generative adversarial network DCGAN discriminator network framework; the low-resolution image is used as an input and is sent into a generation network for processing; and the obtained output is sent to the adversarial network for judgment, a judgment result is fed back to the generative network through a loss function, the steps are repeated until the adversarial network is judged to be qualified, the generative network can generate a clear image, and then super-resolution reconstruction of a low-resolution image is completed by using the trained generative network. The method is mainly applied to image processing occasions.

Description

technical field [0001] Belonging to the field of video and image processing, it involves the improvement of image super-resolution reconstruction algorithm and the fusion of deep learning theory and image super-resolution reconstruction, the implementation of dense residual convolutional neural network and generative confrontation network in the field of high-resolution image reconstruction with application. Specifically, it relates to a single image super-resolution reconstruction method based on a densely connected generative adversarial network. Background technique [0002] Image super-resolution refers to the process of obtaining corresponding high-resolution images by using single or multiple low-resolution degraded image sequences. In many practical applications in the field of image processing, people often hope to obtain high-resolution original images, because high-resolution images mean higher pixel density, which can provide richer high-frequency detail informat...

AI Technical Summary

Problems solved by technology

From the quantitative evaluation results, the evaluation value obtained by SRGAN is not very high

Images