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논문 기본 정보

자료유형
학술저널
저자정보
Sajid Hussain (Korea Institute of Science and Technology Information) Jung-Hun Shin (Korea Institute of Science and Technology Information) Syed Asif Raza Shah (Sukkur IBA University) Kum-Won Cho (Kumoh National Institute of Technology)
저널정보
한국멀티미디어학회 멀티미디어학회논문지 멀티미디어학회논문지 제26권 제12호
발행연도
2023.12
수록면
1,626 - 1,641 (16page)
DOI
10.9717/kmms.2023.26.12.1626

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초록· 키워드

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Super-resolution (SR) stands as a prominent challenge in computer vision with diverse applications. Generative adversarial networks (GANs) yield impressive SR outcomes by restoring high-quality images from low-resolution input. However, GAN-based SR (particularly generators) have high memory demands, leading to performance degradation and energy consumption, making them unsuitable for resource-limited devices. Addressing this concern, our paper introduces a novel and efficient SR-GAN (generator) model architecture by strategically leveraging knowledge distillation, which results in reducing storage demands by 58% while enhancing performance. Our approach involves extracting feature maps from a resourceintensive model to design a lightweight model with minimal computational and memory requirements. Experiments across several benchmarks demonstrate that the proposed compressed model outperforms existing knowledge distillation-based techniques, particularly in regard to SSIM, PSNR, and overall image quality in x4 super-resolution tasks. In the future, this compressed model will be implemented and benchmarked with existing models in resource-limited devices such as tablet and wearing devices.

목차

ABSTRACT
1. INTRODUCTION
2. RELATED WORK
3. RESEARCH OBJECTIVES
4. METHODOLOGY
5. EXPERIMENT
6. CONCLUSION
REFERENCES

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