Vu, K.H.; Nguyen, D.P.; Nguyen, D.D.; Pham, H.-A. Investigation into Perceptual-Aware Optimization for Single-Image Super-Resolution in Embedded Systems. Electronics2023, 12, 2544.
Vu, K.H.; Nguyen, D.P.; Nguyen, D.D.; Pham, H.-A. Investigation into Perceptual-Aware Optimization for Single-Image Super-Resolution in Embedded Systems. Electronics 2023, 12, 2544.
Vu, K.H.; Nguyen, D.P.; Nguyen, D.D.; Pham, H.-A. Investigation into Perceptual-Aware Optimization for Single-Image Super-Resolution in Embedded Systems. Electronics2023, 12, 2544.
Vu, K.H.; Nguyen, D.P.; Nguyen, D.D.; Pham, H.-A. Investigation into Perceptual-Aware Optimization for Single-Image Super-Resolution in Embedded Systems. Electronics 2023, 12, 2544.
Abstract
Deep learning has been introduced to single-image super-resolution (SISR) in the last decade. These techniques have taken over the benchmarks of SISR tasks. Nevertheless, most architectural designs necessitate substantial computational resources, leading to a prolonged inference time on embedded systems or rendering them infeasible for deployment. This paper presents a comprehensive survey of plausible solutions and optimization methods to address this problem. Then, we propose a pipeline that aggregates the latter in order to enhance the inference time without significantly compromising the perceptual quality. We investigate the effectiveness of the proposed method on a lightweight GAN-based perceptual-oriented model as a case study. The experimental results show that our proposed method leads to significant improvement in the inference time on both Desktop and Jetson Xavier NX, especially for higher resolution input sizes on the latter, thereby making it deployable in practice.
Keywords
Single-Image Super-Resolution (SISR); Deep Learning; Quantization; Network Pruning; Knowledge Distillation
Subject
Computer Science and Mathematics, Computer Vision and Graphics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
