Earticle

초록

영어

The ASV-SR method introduces an innovative approach to single-image super-resolution (SISR) by integrating adaptive Stochastic Variation within a diffusion model. This combination effectively captures pixel interactions and various patterns, addressing long-range dependencies in images and overcoming the limitations of traditional deterministic SISR methods. Extensive evaluations on diverse image datasets, including PSNR, SSIM, and LPIPS metrics, reveal that the proposed model outperforms current state-of-the-art techniques. Additionally, the incorporation of a modified SWIN transformer (MST) enhances feature extraction, improving the model's adaptability and efficiency in tackling SISR challenges. This comprehensive approach underscores the significance of incorporating stochastic processes like stochastic variation to advance image super-resolution.

목차

Abstract
I. INTRODUCTION
II. PROPOSED METHOD
III. EXPERIMENT AND RESULTS
IV. CONCLUSION
ACKNOWLEDGMENT
REFERENCES

키워드

저자

• Aradhana Mishra [ Department of Information and Communication Engineering Chosun University ]
• Taeyoung Na [ Media Gen AI Team SK telecom ]
• Bumshik Lee [ Department of Information and Communication Engineering Chosun University ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  한국차세대컴퓨팅학회 학술대회

• 간기

  반년간

• 수록기간

  2021~2025

• 십진분류

  KDC 566 DDC 004