# Single Image Super-Resolution Method Using CNN-Based Lightweight Neural Networks
## 2 Related Works  
Deep learning-based SR methods have shown high potential in the field of image interpolation and restoration, compared to the conventional pixel-wise interpolation algorithms. [PERSON] et al. proposed a three-layer CNN structure called super-resolution convolutional neural network (SR-CNN) [9], which learns an end-to-end mapping from a bi-cubic interpolated low-resolution image to a high-resolution image. Since the advent of SR-CNN, a variety of CNN networks with deeper and denser network structure [10; 11; 12; 13] have been developed to improve the accuracy of SR.  
In particular, [PERSON] et al. proposed a ResNet [11] for image classification. Its key idea is to learn residuals through global or local skip connection. It notes that ResNet can provide a high-speed training process and prevent the gradient vanishing effects. In addition to ResNet, [PERSON] et al. proposed densely connected convolutional networks (DenseNet) [12] to combine hierarchical feature maps available along the network depth for more flexible and richer feature representations. [PERSON] et al. proposed an artifacts reduction CNN (AR-CNN) [14], which effectively reduces the various compression artifacts such as block artifacts and ringing artifacts on Joint Photographic Experts Group (JPEG) compression images.  
[PERSON] et al. proposed a super-resolution scheme with very deep convolutional networks (VDSR) [15], which is connected with 20 convolutional layers and a global skip connection. In particular, the importance of receptive field size and the residual learning was verified by VDSR. [PERSON] et al. proposed a SR-ResNet [16], which was designed with multiple residual blocks and generative adversarial network (GAN) for improving visually subjective quality. Here, a residual block is composed of multiple convolution layers, a batch normalization, and a local skip connection. [PERSON] et al. exploited enhanced deep super-resolution (EDSR) and multi-scale deep super-resolution (MDSR) [17]. In particular, as these networks have been modified in a way of removing the batch normalization, it can reduce graphics processing unit (GPU) memory demand by about 40% compared with SR-ResNet.  
[PERSON] et al. proposed an image super-resolution using dense skip connections (SR-DenseNet) [18] as shown in Figure 1. Because SR-DenseNet consists of eight dense blocks and each dense block contains eight dense layers, this network has a total of 67 convolution layers and two deconvolution layers. Because the feature maps of the previous convolutional layer are concatenated with those of the current convolutional layer within a dense block, total number of the feature map from the last dense block reaches up to 1040 and it requires more memory capacity to store the massive network parameters and intermediate feature maps.  
On the other hand, the aforementioned deep learning-based SR methods are also applied to compress raw video data. For example, Joint Video Experts Team (JVET) formed the Adhoc Group (AhG) for deep neural networks based video coding (DNNVC) [19]in 2020, which aims at exploring the coding efficiency using the deep learning schemes. Several studies [20; 21; 22] have shown better coding performance than the-state-of-the-art video coding technologies.