# A Comprehensive Survey on Real-Time Image Super-Resolution for IoT and Delay-Sensitive Applications
## 3 Real-Time Systems and Real-Time SR
### Deep Learning-Based Models for Real-Time Super-Resolution
#### 3.4.1 Deep Convolutional Neural Network-Based Models for Real-Time Super-Resolution  
Initiallially, in ref. [49], an SR convolutional neural network (SRCNN) was described that uses end-to-end learning to map LR images to HR images. The SRCNN re-interprets existing sparse-coding-based SR approaches in the context of a deep convolutional neural network. Unlike traditional sparse coding techniques, which process each component independently, the SRCNN optimizes all layers simultaneously. The network is also light and quick, making it ideal for real-time web applications. Despite its end-to-end learning capabilities, the SRCNN's performance is limited by its shallow convolutional design and tiny receptive field. Following this [76], a very deep convolutional network (VDSR) for single-image SR (SISR) was built using the VGG-net [77]. The main idea underlying the VDSR is to improve accuracy by dramatically expanding the network depth. The VDSR design has 20 weight layers, which are built with a deep structure of recursively cascaded tiny filters. To expedite convergence, the network learns residuals at extraordinarily high rates--up to 10,000 times faster than those utilized in the SRCNN [49]. Additionally, gradient clipping is used to preserve training stability. The VDSR model does, however, have a restriction in that it is based on a fixed-size receptive field. In addition [78], a fast SR convolutional neural network (FSRCNN) has been introduced to speed up the SRCNN. The FSRCNN redesigns the SRCNN in three ways: it adds a deconvolution layer at the end to reduce computation by directly upsampling images; it uses a mapping layer with smaller filters after shrinking the input features to improve efficiency; and it optimizes parameters for real-time CPU performance. These enhancements enable the FSRCNN to outperform SRCNN in terms of processing speed and image restoration quality. However, these methods fail to produce better image quality, and the resulting SR images often appear blurred or exhibit edge halos. To address these issues, [PERSON] et al. [55] introduced an adaptive importance learning scheme model (VDSR-f22+ILT) proposed as an addition to the VDSR model, which incorporates adaptive importance learning (AIL) for SISR. During training, the model constantly adjusts the relevance of image pixels based on the loss function. A custom built significance penalty function was utilized to gradually raise the emphasis on individual pixels by solving a convex optimization problem. Training begins with simpler pixels and progresses to more complex ones, allowing the network to develop a robust initial capability. The primary novelty of the VDSR-f22+ILT model is its new training strategy, which incorporates significance learning into a joint optimization process during the training phase.  
Recently, ref. [79] presented a novel residual local feature network (RLFN) for SISR. The RLFN learns residual local features using three convolutional layers and a reduced feature aggregation algorithm to find a compromise between performance and processing time. Furthermore, a contrastive loss function is used based on the idea that picking intermediate characteristics from the feature extractor might improve model performance. To improve the performance even more, a multi-stage warm-start training strategy is used with pretrained weights from previous stages. These achievements allowed the RLFN to win first place in the NTIRE 2022 efficient SR competition.