# RETRACTED: Image Super-Resolution Algorithm Based on Dual-Channel Convolutional Neural Networks
Accordingly, the use of high-resolution images is required in some areas and fields of research. In order to solve the problems caused by low image quality, Single Image Super Resolution (SISR) technology is used to transform a single Low-Resolution (LR) image into a High-Resolution (HR) image containing rich high-frequency information. There are wide applications for this technology in the research fields of object detection, satellite image, medical image and face recognition [1; 2; 3; 4]. Traditional _SISR_ methods have included interpolation methods based on the sample extraction theory, such as Bicubic Interpolation [5] and Bilinear Interpolation [6]. The image reconstruction is based on methods including the Iterative Back Projection (IBP) method [7], the Projection Onto method (PO) [8], the Maximum A Posteriori method (MAP) [9], and so on. Based on learning methods such as embedded neighborhood [10], the regression or mapping relationship between HR and LR blocks has been understood by using the concept of geometric similarity. In sparse representation based on the interrelated approach, [PERSON] et al. [11] and [PERSON] et al. [12] reconstructed HR image blocks and HR images by strengthening the similarity between LR and HR image blocks and their corresponding real dictionaries, so that the sparse representation of the LR block and the super-completed HR dictionary can be used to reconstruct HR image blocks and then connect HR images. A complete high-resolution image is obtained like a block [13; 14; 15; 16]. In recent years, deep learning has achieved remarkable results in the research field of image super resolution, benefiting from the powerful feature characterization [17] of deep learning, which is more effective than traditional methods. [PERSON] et al. [18] first proposed the application of the Super Resolution using Convolutional Neural Networks (SRCNN) algorithm to super-resolution images. Compared with traditional methods, the simple network structure obtains the ideal super-resolution; however, there are limitations of the simple network structure. Firstly, it is dependent on the context information of small image blocks. Secondly, the training convergence is too slow, and the time complexity is high. Thirdly, the simple network only can be used for a single-scale super resolution (SR) procedure. [PERSON] et al. [19] proposed the Fast Super-Resolution Convolutional Neural Network (FSRCNN) by reducing the speed training of the network parameters. FSRCNN used eight layers of network structure, making it deeper than SRCNN; moreover, instead of Bicubic Interpolation, the anti-coiling layer was used on the last layer of the network. Finally, FSRCNN has achieved success in the convergence and super-resolution reconstruction field. Considering the slow convergence and shallow network of SRCNN and FSRCNN networks, [PERSON] et al. [20] proposed an image super-resolution algorithm (EEDS) based on end-to-end and shallow convolutional neural networks that has achieved better performance than others. However, because the deep network cannot fully extract the features of an _LR_ image in the feature extraction stage; the loss of useful information and long-term memory content during the reconstruction process becomes serious when the feature of the up-sampling process is nonlinear mapping, as this causes the effect of super resolution to be reduced by the deep network. However, generally speaking, the shallow network master is the main problem. Moreover, when restoring the main components of LR images, the fast convergence of the network can be limited if too many parameters are used. [PERSON] et al. [21] proposed a highly accurate single-image super-resolution method named Very Deep Networks for Super Resolution (VDSR). By using a very deep convolutional network of _VGG-net_[22] in image classification, the model employs cascaded small filters in a deep-network structure, using 20 weight-layers to efficiently utilize the context information of the large image region. Moreover, [PERSON] et al.