# RETRACTED: Image Super-Resolution Algorithm Based on Dual-Channel Convolutional Neural Networks
[23] proposed the Deep Recursive Convolutional Network (DRCN) for image super resolution. The network uses a very deep recursive layer (as many as 16 recursions), as increasing the recursion depth can improve the performance without the need to introduce additional parameters to additional convolutions. In order to prevent the explosion and disappearance of the gradient, as well as to reduce the difficulty of training, the recursive monitoring and skipping connection methods are far more effective than previous methods. Recently, [PERSON] et al. [24] proposed the Gradual Up-Sampling Network (GUN) method, which is based on a deep convolutional neural network. Thismethod uses a gradual process to simplify the direct \(SR\) problem into a multi-step sampling task that employs very small magnification at each step. The Enhanced Deep Residual Networks for Single-Image Super Resolution (EDSR) and the Multi-Scale Deep Super-Resolution (MDSR) network were proposed by [PERSON] et al. [25] among others. The model is optimized by removing unnecessary modules from the residual network to significantly enhance the performance of the model. Moreover, by extending the size of the model to further improve the performance, _MDSR_ can reconstruct HR images with different magnification factors using a network model. [PERSON] et al. [26] proposed a very deep Memory Network (MemNet) for image restoration, which introduces memory blocks consisting of a recursive unit and a gate control unit that mine persistent memory through an adaptive learning process. The representation and output from previous memory blocks are connected and sent to the gate control unit. The gate control unit is adaptive to control memory [27; 28] and controls how many previous states should be retained and how many current states should be stored to achieve superior performance in super-resolution tasks [29; 30]. By exploring the above methods and combining them with MemNet [26] and Deep Residual Network (ResNet) [31], [PERSON] et al. [32] proposed an enhanced algorithm of image super-resolution based on Dual-Channel Convolution Neural Network (DCCNN), related to SRCNN and EEDS to solve the above problems. The shallow channel is mainly used to restore the overall outline of the original image and to achieve fast convergence performance. By adjusting the parameters of the three-layer network from the shallow channel, it can quickly converge while ensuring the restoration of the main components from the image. By contrast, deep channels are used to extract detailed texture information from LR images. Deep channels are divided into three steps: feature extraction and mapping, up-sampling, and long-term and short-term memory block reconstruction. Because there are fewer network layers in the extraction stage of the original model, the local sensing field of the image is too small, and the full LR image feature extraction will lead to the final SR effect. In order to avoid loss of important high-frequency content, the proposed model increases the residual layer on the original basis by increasing the number of network layers in the process; it also reduces the LR feature mapping dimension, such that the residual layer can learn edge and texture information of the image better than the common stacked convolution, and the increased network depth avoids the network. It is difficult to train the problem, meaning that the feature can be directly transmitted to the lower level so as to optimize the gradient vanishing problem and make it easier for the network to enhance the training performance. During the up-sampling phase, because the sampling operation is an important part of the model, the goal is to increase the space span to the target of the HR size. In order to get good results, a 1.8-1 filter is used to increase the number of dimensions to 64 after the mapping is complete. In addition, deconvolution is used to achieve the sampling rather than manual designing.