Image Super-resolution Reconstruction Based On Convolutional Neural Network

The image is limited by the imaging system,imaging device,etc.during the formation process,resulting in degradation of the image quality.The quality of the imaging system and the imaging device usually affects the degree of image clarity.In order to improve the sharpness of the image without increasing the input of hardware cost,the concept of super-resolution reconstruction of the image is obtained.Image super-resolution reconstruction technology overcomes the dependence of high-resolution images on imaging hardware.Super-resolution reconstruction of images refers to the reconstruction of high-resolution images from low-resolution images of reduced quality by means of digital signal processing.Since the concept of image super-resolution was first proposed,in the following 30 years,countless scholars have proposed various super-resolution reconstruction algorithms.In general,image super-resolution methods can be roughly divided into three categories: Interpolation-based method,method of reconstructing with prior constraints,and method of learning.Because the learning-based approach can learn from external databases to introduce more prior knowledge,the development of image super-resolution reconstruction is mainly focused on learning-based methods.With the rise of artificial intelligence and deep neural networks,convolutional neural networks have been successfully applied to image reconstruction.The deep learning method applied to image super-resolution reconstruction using convolutional neural network,referred to as SRCNN algorithm,successfully combines neural network with image super-resolution reconstruction method and opened up the road of image reconstruction in deep learning field.In this paper,through the research of classical convolutional neural network,the SRCNN algorithm is computationally intensive and the training time is long.From the perspective of reducing the parameters to prevent over-fitting,this paper proposes a new super-resolution reconstruction model,which is based on the convolutional neural network and has the following innovations: 1.Deepen the depth of the network,from the original 3-layer convolution to 6 layers,and finally the network depth has 12 layers;2.Join the pooling layer after feature extraction to reduce the dimension of the extracted features;As the depth of the network deepens,the number of parameters increases,and the operation of the Local Response Regularization Layer(LRN)can effectively prevent over-fitting,and at the same time speed up the convergence of the network;4.In the final reconstruction phase,The up sampling reconstruction operation is performed using deconvolution.The experimental results show that the improved method has better reconstruction effect.