# A Comprehensive Survey on Real-Time Image Super-Resolution for IoT and Delay-Sensitive Applications
## 6 Conclusions  
This survey provides an in-depth examination of the majority of studies on real-time SR published up to 2024, presenting a structured and comprehensive taxonomy to classify and analyze these approaches. It delves into the intricacies of various methods, offering a nuanced discussion of their strengths and limitations as highlighted in the reviewed publications. Furthermore, it identifies and outlines the most frequently adopted strategies for addressing specific challenges in real-time SR, enabling a detailed understanding of the problem-solving methodologies employed in this domain.  
Over the past decade, research in real-time SR has made notable strides, driven by advancements in deep learning and computational techniques. Nevertheless, the number of deep learning-based SR methods that achieve real-time performance remains limited. This shortfall can largely be attributed to the significant computational demands of these models, which often struggle to balance rapid processing speeds with high-quality outputs, particularly on resource-constrained devices. The challenge is further compounded by the difficulty of designing architectures that are lightweight yet powerful enough to handle the intricate details required for SR tasks. These limitations underscore the need for novel strategies that combine computational efficiency with robust performance.  
This review highlights several promising directions for future research. For instance, by integrating model compression techniques such as pruning, quantization, and knowledge distillation, hardware-aware NAS-based approcahes could yield lightweight, high-performance models optimized for real-time applications. Additionally, developing adaptive algorithms capable of dynamically adjusting model complexity based on available computational resources could significantly expand the applicability of real-time SR techniques. Domain-specific SR methods, tailored to the unique requirements of applications such as medical imaging, satellite imagery, or video streaming, also hold immense potential for further refinement and optimization.  
By consolidating current knowledge and providing critical insights, this survey aims to serve as a foundational reference for researchers and developers seeking to enhance SR solutions. Whether the goal is improving runtime efficiency, achieving higher accuracy, or addressing application-specific challenges, this review offers practical guidance and a roadmap for advancing real-time SR. Ultimately, we hope this work inspires new innovations that will address existing gaps and unlock the full potential of super-resolution technology.  
This research was supported by the Culture, Sports, and Tourism R&D Program through the Korea Creative Content Agency grant funded by the Ministry of Culture, Sports, and Tourism in 2023 (Project Name: Cultural Technology Specialist Training and Project for Metaverse Game, Project Number: RS-2023-00227648, Contribution Rate: 100%).  
The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.  
The authors declare no conflicts of interest.