# A Comprehensive Survey on Real-Time Image Super-Resolution for IoT and Delay-Sensitive Applications
## 1 Introduction  
Image super-resolution (SR) is a computer vision task focused on enhancing the resolution of images by transforming low-resolution (LR) visions into high-resolution(HR) ones [1, 2, 3, 4, 5]. To better understand and interpret the content of images, it is a significant challenge for researchers to derive high-quality images from LR ones [6, 7, 8, 9]. SR technology has significant applications in various fields, particularly in medical imaging, where it enhances image quality for improved diagnosis and analysis. In medical imaging, SR techniques are employed to increase the resolution and clarity of various types of scans, including MRI, CT, and ultrasound [10, 11]. This enhancement facilitates more accurate detection and diagnosis of medical conditions, potentially leading to better patient outcomes. Similarly, in the fields of surveillance and security, SR techniques play a pivotal role in improving video and image clarity, which is essential for enhancing situational awareness and ensuring public safety. By increasing the resolution of low-quality footage, these methods enable more effective monitoring in real-time scenarios, such as traffic surveillance, border security, and crowd management [12, 13, 14]. In addition, remote sensing and satellite imagery also benefit greatly from SR methods, which enhance image resolution for more accurate analysis and mapping. This improvement is critical for scientific fields such as astronomy,microscopy, and geology, where HR images lead to more precise observations and deeper insights [15; 16; 17].  
On the other hand, with the widespread adoption of edge devices and real-time systems, the need for real-time SR approaches has increased. The primary objective of real-time image SR is to estimate an SR image from a given real-time LR image, ensuring that the generated SR image closely resembles the true HR image [18; 19; 20; 21]. Currently, supervised learning-based SR networks are effective at increasing the resolution of real-time images. However, constructing real-time SR images presents significant challenges, such as high computational costs, which necessitate lightweight models [3; 4; 15; 16; 22]. These lightweight models have taken massive attention on the real-time SR domain [3; 4; 23; 24; 25]. In recent years, numerous researchers have published various reviews on single-image super-resolution (SISR) approaches that utilizes deep learning [1; 2; 26; 27]. In ref. [28], the authors first categorized SR-based deep networks into nine distinct groups, including linear [29], residual, multi-branch [30], recursive, progressive, attention-based, and adversarial designs [31]. In addition, [PERSON] et al. [32] categorized lightweight SR approaches into six groups: convolution-based, residual, dense, distillation, attention, and extremely lightweight models. Also, in [33], the authors reviewed the latest SISR methods and organized them into four main categories: RSISR based on degradation modeling, RSISR based on image pairs, RSISR based on domain translation, and RSISR based on self-learning. Furthermore, in [14], the authors provide an in-depth discussion of blind image SR by categorizing existing methods into three classes according to the degradation modeling and the data employed in solving the SR model [34].  
However, these reviews have generally focused on frameworks that may or may not be deployable as lightweight models [35], without considering the specific applications for which the models are used in real time [36]. Additionally, while previous surveys have examined existing SR methods from various perspectives [37], to the best of our knowledge, none have specifically focused on real-time SR for delay-sensitive applications [38]. Considering this, we aim to review and summarize advancements in the field of real-time SR for delay-sensitive applications.