Model Compression and Acceleration for Single Image Super-Resolution

DOI

10.2991/978-94-6463-986-5_65How to use a DOI?

Keywords

Single Image Super-Resolution; Model Compression; Lightweight Network Architecture; Deep Learning

Abstract

While deep learning-based methods for Single Image Super-Resolution (SISR) have consistently set new performance benchmarks, their substantial computational and memory footprints pose a significant barrier to deployment on resource-constrained devices. This challenge is particularly acute for SISR, a low-level vision task highly sensitive to the preservation of fine-grained texture details. To bridge this critical gap, two primary strategies are investigated: the design of lightweight network architectures and the application of model compression, focusing on network pruning and parameter quantization. This paper provides a critical investigation into why general-purpose compression algorithms, often developed for high-level vision tasks, frequently yield suboptimal results for SISR. This paper analyzes the inherent challenges, such as the structural constraints imposed by residual connections on pruning and the unique statistical distributions of feature maps that complicate quantization. Ultimately, the objective of this paper is to elucidate the intricate trade-offs among reconstruction fidelity, model size, and actual on-device latency, providing a principled foundation for designing genuinely efficient SISR models for real-world applications.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Xuanzhen Li
PY  - 2026
DA  - 2026/02/18
TI  - Model Compression and Acceleration for Single Image Super-Resolution
BT  - Proceedings of the 2025 International Conference on Electronics, Electrical and Grid Technology (ICEEGT 2025)
PB  - Atlantis Press
SP  - 631
EP  - 640
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-986-5_65
DO  - 10.2991/978-94-6463-986-5_65
ID  - Li2026
ER  -