# A Comprehensive Survey on Real-Time Image Super-Resolution for IoT and Delay-Sensitive Applications
## 5 Future Roadmap  
Recent advances in SISR technology are significantly improving image quality. Nonetheless, there is still room for growth in a multitude of sectors. Deep learning models are likely to improve in the future, particularly in terms of efficiency and real-time inference. The most anticipated future directions include advancements in model compression techniques, the exploration of adaptive and dynamic approaches, and the development of domain-specific real-time SR. Figure 7 shows the real-time SR technique improvements through various means expected to see in the future. Advancements in deep learning models indicate that deep learning will continue to drive breakthroughs in SR research. Deep learning is expected to further enhance SR by developing models that explore sophisticated architectures, such as transformer-based lightweight structures, which represent a new frontier by employing enhanced attention mechanisms to improve both global context and local details while maintaining a balance of feature richness and network complexity. This innovation is critical for allowing SR in resource-constrained situations like edge devices and real-time applications. Simultaneously, effective neural networks can be tuned using techniques such as parameter pruning, quantization, and knowledge distillation, which reduce model complexity while retaining high output quality. In addition to these developments, lightweight diffusion models, which excel at iterative picture refining, have shown outstanding potential for SR applications by combining precision and efficiency. These advances are redefining the landscape of SR, assuring high-quality performance while tackling issues in real-time and resource-constrained applications. In addition, model compression techniques are one of the key strategies for real-time SR, which aims to provide high-quality results in a timely way by utilizing advanced quantization, pruning, and knowledge distillation approaches to reduce model size and inference time without compromising the quality of output images. Quantization involves decreasing the accuracy of the model's weights and activations, which reduces memory and processing needs. Pruning gradually eliminates less-essential weights from the model, substantially simplifying its design while preserving performance. Knowledge distillation involves transferring knowledge from a bigger, more complicated model (the instructor) to a smaller, more efficient one (the student), allowing the smaller model to reach comparable levels of accuracy. These approaches are critical for ensuring that real-time SR systems can run quickly while maintaining image quality, making them useful for SISR. Apart from this, the Neural Architecture Search (NAS) for discovering optimal and efficient SISR models is another future direction that falls under model compression techniques. Figure 7: Future improvements in SR. The NAS automates the process of constructing neural networks by looking for the most efficient architecture that meets the unique needs of real-time SR activities. Using the NAS, researchers may uncover optimum designs that balance model complexity, speed, and accuracy. This enables the creation of unique models that are extremely efficient in terms of both computational cost and performance. Future research will most likely focus on merging these model compression approaches with NAS to develop even more optimal models for specific use cases, such as improving LR pictures in medical imaging, increasing satellite imagery quality, or recovering ancient photographs and films. Furthermore, there is a rising interest in adaptive and dynamic algorithms that may modify the amount of compression or model complexity based on the context or available computational resources, broadening the possible applications for real-time SR techniques.