# A Novel Implicit Neural Representation for Volume Data
## 2 Related Work
### Deep Learning and Super-Resolution (SR) Techniques  
Generally, super-resolution (SR) techniques can be clustered into traditional and deep learning methods. In SRCNN [34], an SR model is trained based on CNN to reconstruct HR from a given LR image. However, in terms of image quality, there are some limitations in SRCNN models. A huge amount of stacked layers causes gradient-vanishing issues. To solve those problems, researchers in the Ref. [35] suggested using a very deep convolutional network base on VGG-net. For some of the deep learning techniques in SR, overfitting is highly likely and models can be so big to be stored. To address these issues, a deeply recursive convolutional network (DRCN) [36] is suggested. In DRCN, a convolutional layer is repeated many times and the number of parameters depends on the number of applied recursions. As DRCN uses stochastic gradient descent widely, it cannot converge easily. To overcome the difficulties of the training, they use recursive supervision and skip connection. In the Ref. [37], authors suggest an end-to-end deep neural network that is constituted of an encoder, a fusion module and a decoder. The encoder takes the features of the LR image. Then they apply a Gated Recurrent Unit (GRU)-based module to combine the features. The super-resolution image is reconstructed by the decoder. The Ref. [38] suggests coupled-discriminate GAN (CDGAN). In CDGAN, HR and SR images are taken by a discriminator. The network can learn to discriminate low-frequency images. An unsupervised Image Super-Resolution is suggested in the Ref. [39]. They propose a framework that is based on two parts, unsupervised translation from the original LR image to the reconstructed LR image and supervised SR between reconstructed LR and HR images. In the Ref. [40] the authors present Meta-Transfer Learning for Zero-Shot Super-Resolution (MZSR). As CNNs are mostly limited to the trained supervised data, they are applicable to specific images. On the other hand, with CNNs it is not applicable to extract internal details of the images. To solve the problems they suggest using zero-shot super-resolution to train internal information. To overcome several gradient updates, they apply meta-learning for zero-shot super-resolution. In remote sensing, There are some issues in super-resolution reconstruction using deep learning, such as model training difficulties and blurred image edges. The Ref. [41] proposes a technique with the combination of residual channel attention (CA) to extract deep features. They combine shallow and deep futures, using skip connections to improve the model training. After training, the super-resolution images have sharper edges. In the Ref. [42] authors suggest using a Residual Back-Projection network (RBPNet) to reconstruct SR from extremely low-resolution face images. To generate the low-resolution feature map, RBPNet projects the high-resolution feature map to the low-resolution feature space. Then to make a residual feature map for the low-resolution feature map, they subtract the low-resolution feature map from the original feature map. Finally, to generate high-resolution feature space, the low-resolution feature map is projected into high-resolution feature space. RBPNet produces more precise high-resolution images using residual learning. During the application of SR techniques in low-resolution face images, face structure details cannot be recovered well. Thus, a novel SPatial Attention Residual Network (SPARNet) is suggested by the Ref. [43]. SPARNET uses a spatial attention mechanism to enable convolutional layers to focus on key face structures and pay less attention to less important details. The results show that their technique can detect key face structures well for extremely low-resolution (16 \(\times\) 16) face images. They also extend SPARNet to SPARNetHD which can generate super-resolution face images (512 \(\times\) 512). In the Ref. [44], the authors address the heavy computation and the high number of parameters of current SR techniques.