Research On Image Super-resolution Algorithm Based On Detail Perception Loss Function

With the progress and development of science and technology,intelligent life and production have gradually entered people's sight,and related technologies have been widely concerned and studied.As the accessible data and information in life and production,images and videos are often used to deal with various practical prob-lems.Therefore,the demand for high-quality images and videos is growing.As an extremely important image quality improvement technology,image super-resolution has become an important research direction in the field of computer vision and im-age processing.Single image super-resolution is the technology of reconstructing high-resolution image from low-resolution image.The key of this research is to re-store the original image pixels.However,based on the research of the current typical algorithms,it is found that the reconstruction of high-frequency image detail is an urgent problem.Focusing on this problem,this paper combines the understanding of low-frequency information and high-frequency information of image,uses the idea of function approximation and designs an effective algorithm.The main contributions of this paper are as follows:(1)Aiming at the problem of image pixel restoration,this paper regards im-age super-resolution technology as the approximation of super-resolution image to high-resolution image,which belongs to the category of two-dimensional function approximation essentially.The image consists of low frequency information and high frequency information.Based on the framework of convolutional neural net-work,the low-frequency information transfer pathway is designed to approach the low-frequency information of high-resolution image.At the same time,the high-frequency information prediction pathway is designed to separate and predict the corresponding high-frequency information from the low-resolution image,and ap-proach the high-frequency information of the high-resolution image.Through the fusion of the two kinds of information,the high-resolution image can be reconstruct-ed.The model uses the L1 norm of image difference as the loss function to ensure that the reconstructed image approaches the high-resolution image at the pixel level.(2)To solve the problem of image detail information reconstruction,this paper uses the knowledge of wavelet analysis to model image detail features.A detail loss function is designed to measure the similarity of image detail features.Through the similarity of features,the parameters of super-resolution model are optimized.This enables the model to approach the image at the level of detail features in the process of image reconstruction and ensures the reconstruction of high-frequency details.(3)In this paper,we analyze the hyperparameters and performance of the model from the experiments of depth,width,parameters,residual block ablation,loss function comparison and so on.Through the image reconstruction experiments of Set5,Set14,BSDS100 and Urban 100 data sets,it is found that the proposed model has achieved excellent results in image detail reconstruction compared with the advanced image super-resolution method.