Study And Application Of Image Super-resolution Reconstruction Based On SRGAN

Image super-resolution reconstruction is to improve the image resolution and build a higher quality image through image processing technology without changing the imaging method.The technology has been applied in many fields such as medical treatment,detection,communication,public safety and remote sensing imaging.In recent years,with the rapid development of machine learning,especially deep learning technology,image super-resolution reconstruction technology based on deep learning model has become the mainstream model,which promotes the rapid development of research and application of image super-resolution technology.This thesis is the exploration of this hot field.Firstly,the classical SRGAN(Photo-Realistic Single Image Super-Resolution Using a General Adverse Network)model is studied and improved.Through comparative experiments,the effectiveness of the improvement is verified.Then,taking the super-resolution reconstruction of road traffic sign image as the application background,the feasibility of image super-resolution application is verified.The main work of this thesis is as follows:(1)By consulting a large number of data,SRGAN algorithm is selected as the basis for research,and then the activation function of SRGAN is improved: Swish and Mish functions are used to replace the PRelu and Leaky Relu functions of SRGAN respectively.The comparative experiments show that the improved model has lower loss value of loss function,faster convergence speed and higher training stability..(2)In order to solve the problems of low PSNR and artifacts in the image reconstructed by SRGAN model,a new model SDSRGAN(Squeeze-and-Excitation Deep Recursive Residual Network Generative Adversarial Networks)is proposed.The main features of the model are as follows: firstly,a new residual block is designed.Se module is added to the residual block to quickly extract important image features,improve the global receptive field,and remove the batch normalization layer to reduce the amount of parameters and computational complexity.At the same time,the position of convolution layer and activation layer is inverted to improve the performance of residual block.Secondly,the recursive network and residual network are combined to deepen the network layer without increasing the parameters to improve the quality of image reconstruction.Finally,Adam optimizer is changed to RAdam optimizer to facilitate model training.Experimental results show that SDSRGAN performs better than other classic super-resolution algorithms in both objective evaluation index PSNR /SSIM and subjective evaluation.(3)The image super-resolution reconstruction technology is applied to the reconstruction of traffic sign image to improve the accuracy of traffic sign recognition in assisted driving.According to the characteristics of traffic sign image and the real-time requirement of traffic recognition field,an algorithm model TRSRGAN(Traffic Sign Super-Resolution Generative Adverse Networks)based on traffic sign image is designed.The residual block is improved in the model,and the mixed depth convolution is used in the residual network.In the loss function,the Charbonnier function is used as the content loss to improve the accuracy of the reconstructed image,and the perceptual loss is calculated by the eigenvalues of the pre trained VGG network before activation,so that the reconstructed traffic sign image has better texture color.Finally,for the convenience of training,a traffic sign data set CQUNJ205 is made,and Cutblur’s image enhancement method is used to improve the network performance.The experimental results show that the traffic sign image reconstructed by the improved model is better than other contrast algorithms in the objective evaluation index,and the texture details and color brightness are also improved.