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

Images are one of the most important ways for humans to obtain information,and how to obtain high-quality images has become an increasingly urgent problem.Due to the limitations of imaging hardware and the interference of objective factors such as light,the quality of images directly obtained by imaging devices is often poor and lacks texture information.It is difficult and costly to obtain high-quality images by improving the hardware indicators of imaging equipment,so low-cost and high-efficiency image super-resolution reconstruction technology emerges as the times require.The technology is widely used,and is currently widely researched and applied in the fields of medicine,public security,aviation and military.Based on the existing research on image super-resolution algorithms,in view of the performance bottlenecks of traditional image super-resolution algorithms such as ignoring high-frequency information of images and unstable image reconstruction results,this paper decides to study a learning-based reconstruction algorithm,mainly researching generative adversarial network applications in the field of image super-resolution,and then in view of the shortcomings of this algorithm in the process of image super-resolution reconstruction,such as ignoring mid-level features and single feature extraction scale,related theoretical research and algorithm improvement are carried out,and two kinds of image super-resolution reconstruction algorithms are proposed.The main research work of this paper can be summarized into the following two aspects:(1)This paper proposes a image super-resolution reconstruction algorithm named CABFF-SRGAN based on channel attention mechanism and feature fusion.Firstly,the channel attention mechanism is introduced into the generative model of the SRGAN algorithm to give more weight to the feature channels with rich high-frequency information,combined with the idea of binary feature fusion,the output features of the basic modules at all levels are integrated to enhance the image super-resolution reconstruction capability of the algorithm.Secondly,the batch normalization layer in the base module is removed to prevent the introduction of artifacts during image reconstruction.Without reducing the classification performance of the discriminant model,the fully connected layer in the discriminant model is replaced by a convolutional layer and a global average pooling layer,which greatly reduces the parameters of the discriminant model.(2)This paper proposes a image super-resolution reconstruction algorithm named SKCB-SRGAN based on selective kernel convolution block.Based on the idea of multi-scale feature extraction of the Inception module,a convolution kernel selection module based on adaptive weight is proposed,and the basic module group is constructed by combining with the channel attention module.In this way,the generation model of SRGAN algorithm is improved and the reconstruction capability of the algorithm for complex structure input is improved.In the discriminative model,the fully connected layers are replaced with less computationally intensive convolutional layers and global average pooling layers to achieve real and fake image classification.In order to verify the superiority of the CABFF-SRGAN and SKCB-SRGAN image super-resolution reconstruction algorithms proposed in this paper,under the three test sets of Set5,Set14 and BSDS100,them are compared with the four commonly used image super-resolution algorithms such as Bicubic,SRCNN,VDSR and SRGAN.Comparative experiments are carried out,and the performance of the algorithm is evaluated using two common objective evaluation indicators: peak signal-to-noise ratio(PSNR)and structural similarity(SSIM).It can be seen from the analysis of the experimental results that the algorithm in this paper obtains higher PSNR and SSIM values,the reconstructed image has richer texture detail information,and the visual effect is better.