Research On Image Super-resolution Reconstruction Algorithm Based On Deep Learning

Images play the role of an efficient information transmitter in today's increasingly frequent human information exchanges,and high-quality images contain more and richer information,which can meet people's needs for more efficient information exchange.However,it has brought about problems such as difficulty in obtaining,transmitting and applying high-quality images caused by the old hardware equipment.Therefore,a technology for obtaining high-resolution images through algorithms is needed.Super-resolution reconstruction technology does not need to replace high-quality imaging equipment,and only uses deep learning models to obtain high-quality images with higher resolution,richer details,and clearer outlines through low-resolution image calculations,which not only meets human needs for high-quality images,It will not increase the economic cost,so it has received widespread attention from scholars and the industry.Therefore,this article will focus on the research and improvement of the image super-resolution reconstruction algorithm based on deep learning.This article focuses on the use of deep learning neural networks in the super-resolution reconstruction problem.Aiming at the problems that traditional network models cannot make full use of deep and shallow features,they cannot effectively save information in long-term sequences during training,and affect the quality of reconstructed pictures.Improved a super-resolution reconstruction architecture based on the cyclic embedded hourglass network,which embeds the threshold cycle unit into the hourglass network for feature extraction.The hourglass network can efficiently integrate deep features with shallow features and the threshold cycle unit can be stored for a long time.The characteristics of the information in the sequence,independently iteratively save the non-linear mapping relationship between the high-resolution and low-resolution images that has a great gain in the reconstruction of the high-resolution image,and maintain the integrity of the image feature information in the learning.Experimental results show that the image reconstructed by this algorithm can effectively remove artifacts and improve subjective visual perception.Compared with other methods,the peak signal-to-noise ratio has increased by 0.20 on average,and the structural similarity value has increased by 0.023.Aiming at the problems of single network model and blurred texture of reconstructed image,this paper improves a super-resolution reconstruction neural network architecture based on multi-channel merged convolution,and solves the single problem of network model by establishing different subnets,including deep layer An hourglass subnet with features fused with shallow features,a threshold loop subnet that preserves the long-term relevance of the network,and a multi-scale convolution subnet with different convolution kernels.The original image expansion channels are input into these three sub-networks,the model is expanded by merging the feature channels of the sub-networks,and the bottom-layer pixel features are collected through jump connections.At the end of the model,sub-pixel convolution is performed to generate a high-resolution image.The experimental results prove that the method in this chapter is better than the traditional method.Compared with the method in Chapter 3,the peak signal-to-noise ratio value is increased by 0.13 on average,and the structural similarity value is increased by 0.012.