# A Comprehensive Survey on Real-Time Image Super-Resolution for IoT and Delay-Sensitive Applications
## 3 Real-Time Systems and Real-Time SR
### Sparse Coding Methods
#### 3.3.3 Re-Parameterization in Real-Time SR  
Re-parameterization is a technique used in real-time SR to simplify the SR issue and make it easier to optimize or estimate. The computational complexity of the issue can be lowered by re-parameterizing it, allowing for real-time performance. There are two types of re-parameterization methods available in real-time SR tasks: scale-space re-parameterization and sub-pixel re-parameterization. The SR issue is altered in scale-space re-parameterization by creating intermediary scales between the LR and HR digital frames. Rather than instantly upsampling the LR input to the intended HR, the re-parameterization process progressively upsamples the input to numerous intermediate scales. By dividing the SR work across different scales, this technique minimizes computing complexity and allows for real-time processing.  
Secondly, sub-pixel re-parameterization is frequently utilized to address the constraints of pixel-based SR approaches. Instead of executing SR at the pixel level, this approach attempts to estimate sub-pixel shifts or features in the LR input. The re-parameterized issue becomes easier to solve by estimating sub-pixel shifts, and the subsequent upsampling procedure can provide more accurate and visually appealing HR images.  
In [73], the authors present an edge-priority-extraction network established with their indicated edge-priority blocks (EPB). The EPB improves network representation by combining numerous branches that include edge information. Additionally, the EPB may be re-parameterized for more efficient inference. To make better use of edge information, the study offers a mixed-priority filter that extracts edge information based on horizontal and vertical priorities, boosting network performance. These filters can adapt and capture edge information using multi-directional derivatives. With this approach, they succeeded in reducing the model's complexity, making it suitable for real-time deployment. In addition, ref. [74] presents a re-parameterized dynamic unit (RDU) as a plug-in component with the goal of improving the performance-inference cost trade-off. During the training phase, the RDU learns to combine numerous re-parameterizable blocks by assessing various input data, hence improving layer-level representation. In the inference step, the RDU is transformed into simple dynamic convolutions capable of capturing both dynamic and static feature maps. These RDUs are then combined to build an information distillation block that enables hierarchical refining and selective fusing of spatial context information. Furthermore, the authors suggest a dynamic distillation fusion (DDF) module that allows for dynamic signal aggregation and communication between hierarchical modules, resulting in additional performance gains with fewer parameters while being less computational.  
Apart from this, [PERSON] et al. [75] compared the performance and efficiency of information distillation to residual learning in lightweight SR models. They developed RepRFN, a re-parameterization-based lightweight SR network that minimizes GPU memory utilization while increasing inference performance. The network uses a multi-scale feature fusion structure to successfully collect different features and high-frequency edges, while redundant modules are removed to reduce complexity without losing performance. A Fourier transform-based loss function is also used to improve the learning of picture frequency components, allowing for effective real-time deployment on edge devices.