Deep Learning Empowered Super-Resolution: A Comprehensive Survey and Future Prospects

• URL: http://arxiv.org/abs/2509.22692v1
• Date: Fri, 19 Sep 2025 17:17:42 GMT
• Title: Deep Learning Empowered Super-Resolution: A Comprehensive Survey and Future Prospects
• Authors: Le Zhang, Ao Li, Qibin Hou, Ce Zhu, Yonina C. Eldar,
• Abstract summary: Super-resolution (SR) has garnered significant attention within the computer vision community, driven by advances in deep learning (DL) techniques.<n>We present an in-depth review of diverse SR methods, encompassing single image super-resolution (SISR), video super-resolution (VSR), stereo super-resolution (SSR), and light field super-resolution (LFSR)
• Score: 104.38752472521917
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Super-resolution (SR) has garnered significant attention within the computer vision community, driven by advances in deep learning (DL) techniques and the growing demand for high-quality visual applications. With the expansion of this field, numerous surveys have emerged. Most existing surveys focus on specific domains, lacking a comprehensive overview of this field. Here, we present an in-depth review of diverse SR methods, encompassing single image super-resolution (SISR), video super-resolution (VSR), stereo super-resolution (SSR), and light field super-resolution (LFSR). We extensively cover over 150 SISR methods, nearly 70 VSR approaches, and approximately 30 techniques for SSR and LFSR. We analyze methodologies, datasets, evaluation protocols, empirical results, and complexity. In addition, we conducted a taxonomy based on each backbone structure according to the diverse purposes. We also explore valuable yet under-studied open issues in the field. We believe that this work will serve as a valuable resource and offer guidance to researchers in this domain. To facilitate access to related work, we created a dedicated repository available at https://github.com/AVC2-UESTC/Holistic-Super-Resolution-Review.

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