An Agile Super-Resolution Network via Intelligent Path Selection

Longfei Jia, Yuguo Hu (), Xianlong Tian, Wenwei Luo and Yanning Ye
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Longfei Jia: Qingshuihe Campus, University of Electronic Science and Technology of China, Chengdu 611731, China
Yuguo Hu: Qingshuihe Campus, University of Electronic Science and Technology of China, Chengdu 611731, China
Xianlong Tian: Qingshuihe Campus, University of Electronic Science and Technology of China, Chengdu 611731, China
Wenwei Luo: Qingshuihe Campus, University of Electronic Science and Technology of China, Chengdu 611731, China
Yanning Ye: Qingshuihe Campus, University of Electronic Science and Technology of China, Chengdu 611731, China

Mathematics, 2024, vol. 12, issue 7, 1-16

Abstract: In edge computing environments, limited storage and computational resources pose significant challenges to complex super-resolution network models. To address these challenges, we propose an agile super-resolution network via intelligent path selection (ASRN) that utilizes a policy network for dynamic path selection, thereby optimizing the inference process of super-resolution network models. Its primary objective is to substantially reduce the computational burden while maximally maintaining the super-resolution quality. To achieve this goal, a unique reward function is proposed to guide the policy network towards identifying optimal policies. The proposed ASRN not only streamlines the inference process but also significantly boosts inference speed on edge devices without compromising the quality of super-resolution images. Extensive experiments across multiple datasets confirm ASRN’s remarkable ability to accelerate inference speeds while maintaining minimal performance degradation. Additionally, we explore the broad applicability and practical value of ASRN in various edge computing scenarios, indicating its widespread potential in this rapidly evolving domain.

Keywords: super resolution; edge computing; accelerated inference; resource-limited; policy network (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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