A Comprehensive Survey on Real-Time Image Super-Resolution for IoT and Delay-Sensitive Applications
In contemporary computer vision, deep learning-based real-time single image super-resolution approaches have gained significant attention for their ability to enhance the resolution of images in real time. These approaches are interconnected with various other computer vision domains, including imag...
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MDPI AG
2024-12-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/1/274 |
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| author | M. J. Aashik Rasool Shabir Ahmad Sevara Mardieva Sumaiya Akter Taeg Keun Whangbo |
| author_facet | M. J. Aashik Rasool Shabir Ahmad Sevara Mardieva Sumaiya Akter Taeg Keun Whangbo |
| author_sort | M. J. Aashik Rasool |
| collection | DOAJ |
| description | In contemporary computer vision, deep learning-based real-time single image super-resolution approaches have gained significant attention for their ability to enhance the resolution of images in real time. These approaches are interconnected with various other computer vision domains, including image segmentation and object detection. Numerous surveys have summarized the state of the image SR domain. However, there is no survey that specifically addresses real-time single image SR on IoT devices. Therefore, in this study, we aim to explore strategies, identify the technical challenges, and outline the future directions of SR research, with a special emphasis on real-time super-resolution techniques. We begin with an overview of the core concepts related to real-time SR, recent challenges, and algorithm classification and delve into potential application scenarios that merit attention. Additionally, we explore the challenges and identify promising research areas related to real-time SR specifically related to IoT devices, highlighting potential advancements, limitations, and opportunities for future innovation in this rapidly evolving field. |
| format | Article |
| id | doaj-art-5df1da1ce9d74ab28ddac0bfc0606518 |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-5df1da1ce9d74ab28ddac0bfc06065182025-01-10T13:15:00ZengMDPI AGApplied Sciences2076-34172024-12-0115127410.3390/app15010274A Comprehensive Survey on Real-Time Image Super-Resolution for IoT and Delay-Sensitive ApplicationsM. J. Aashik Rasool0Shabir Ahmad1Sevara Mardieva2Sumaiya Akter3Taeg Keun Whangbo4Department of IT Convergence Engineering, Gachon University, Sujeong-Gu, Seongnam-Si 461-701, Gyeonggi-do, Republic of KoreaDepartment of IT Convergence Engineering, Gachon University, Sujeong-Gu, Seongnam-Si 461-701, Gyeonggi-do, Republic of KoreaDepartment of IT Convergence Engineering, Gachon University, Sujeong-Gu, Seongnam-Si 461-701, Gyeonggi-do, Republic of KoreaDepartment of IT Convergence Engineering, Gachon University, Sujeong-Gu, Seongnam-Si 461-701, Gyeonggi-do, Republic of KoreaDepartment of IT Convergence Engineering, Gachon University, Sujeong-Gu, Seongnam-Si 461-701, Gyeonggi-do, Republic of KoreaIn contemporary computer vision, deep learning-based real-time single image super-resolution approaches have gained significant attention for their ability to enhance the resolution of images in real time. These approaches are interconnected with various other computer vision domains, including image segmentation and object detection. Numerous surveys have summarized the state of the image SR domain. However, there is no survey that specifically addresses real-time single image SR on IoT devices. Therefore, in this study, we aim to explore strategies, identify the technical challenges, and outline the future directions of SR research, with a special emphasis on real-time super-resolution techniques. We begin with an overview of the core concepts related to real-time SR, recent challenges, and algorithm classification and delve into potential application scenarios that merit attention. Additionally, we explore the challenges and identify promising research areas related to real-time SR specifically related to IoT devices, highlighting potential advancements, limitations, and opportunities for future innovation in this rapidly evolving field.https://www.mdpi.com/2076-3417/15/1/274real-time image SRimage enhancementreal-time systemssingle image super-resolution |
| spellingShingle | M. J. Aashik Rasool Shabir Ahmad Sevara Mardieva Sumaiya Akter Taeg Keun Whangbo A Comprehensive Survey on Real-Time Image Super-Resolution for IoT and Delay-Sensitive Applications Applied Sciences real-time image SR image enhancement real-time systems single image super-resolution |
| title | A Comprehensive Survey on Real-Time Image Super-Resolution for IoT and Delay-Sensitive Applications |
| title_full | A Comprehensive Survey on Real-Time Image Super-Resolution for IoT and Delay-Sensitive Applications |
| title_fullStr | A Comprehensive Survey on Real-Time Image Super-Resolution for IoT and Delay-Sensitive Applications |
| title_full_unstemmed | A Comprehensive Survey on Real-Time Image Super-Resolution for IoT and Delay-Sensitive Applications |
| title_short | A Comprehensive Survey on Real-Time Image Super-Resolution for IoT and Delay-Sensitive Applications |
| title_sort | comprehensive survey on real time image super resolution for iot and delay sensitive applications |
| topic | real-time image SR image enhancement real-time systems single image super-resolution |
| url | https://www.mdpi.com/2076-3417/15/1/274 |
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