Research On Image Super-Resolution Reconstruction Algorithm Based On Generative Adversarial Network

Image is the main carrier of information transmission and presentation.High-resolution image provides a solid foundation for the development of computer vision because of their clear image quality and rich details.However,due to the interference of imaging equipment,external environment,and other factors,the obtained image is relatively blurred.At this time,the definition of the image can be improved by super-resolution reconstruction technology.In recent years,the image super-resolution reconstruction method based on the generated countermeasure network restores the real and natural image,which is more in line with the visual perception of human eyes.However,when reconstructing images with rich texture and color,there will be some phenomena such as blurred details and disordered texture,and it is difficult to generate confrontation network training,and the model is unstable and easy to collapse.In order to solve the problems of unclear image details and disordered texture restored by image super-resolution reconstruction algorithm(SRGAN)generating countermeasure network,a super-resolution reconstruction algorithm based on dense residual network(SRGAN-R)is proposed.Firstly,the batch normalization layer is deleted in the network structure to reduce the computational complexity of the network and ensure that the color and brightness information of the image remains unchanged.The advantages of hierarchical network and dense networks can be used for reference,and then the characteristics of hierarchical networks and dense networks can be quickly expressed.Finally,it is tested on four benchmark data sets and one self-collected medical data set.The results show that compared with the SRGAN algorithm,the proposed algorithm accelerates the reconstruction speed of the model,improves the average PSNR value and SSIM value of the reconstructed image by 0.488dB and 0.0246 respectively,and the details of the reconstructed image are clearer and the texture is more realistic.Although the algorithm improves the quality of the reconstructed image,it also requires the stability of the algorithm in the actual application scene.Therefore,this paper further optimizes the algorithm.To address the problems of difficult convergence and unstable training of generated countermeasure network,a super-resolution reconstruction algorithm based on optimized loss function(SRWGAN-R)is proposed based on SRGAN-R.Firstly,the WGAN is used to optimize the algorithm in the discrimination network to stabilize the training of the network,and the optimized residual block is used to extract the deep features.Then,in the loss function,the optimized perception loss,content loss,confrontation loss,and texture loss are used to supervise the model training.Finally,four benchmark data sets and one self-collected medical data set are tested.The results show that the proposed algorithm converges faster and is more stable than the SRGAN algorithm.At the same time,the average PSNR value and SSIM value of the reconstructed image are improved by 1.088db and 0.0329 compared with the SRGAN algorithm,and 0.58dB and 0.0187 compared with the SRGAN-R algorithm.The texture of the reconstructed image is richer,the brightness and color information is more consistent with the original image,and the quality of the reconstructed image is better improved.