Earticle

초록

영어

Recently, Implicit Neural Representations (INRs) have been gaining attention as an approach where neural networks learn a continuous function that takes coordinates as input and outputs the color values at those locations. Using INRs allows for reconstructing images of any size without constraints on spatial resolution. As a result, it has emerged as a promising method for super resolution, enabling a single neural network to represent images at all resolutions. However, existing research on INR-based super-resolution still lags behind other deep learning methods in terms of performance. This is because a single neural network, which takes uniform coordinate values as input, faces challenges in representing information of varying complexity across different regions of an image. Therefore, we propose a method to improve super-resolution performance by decomposing an image into variable-sized blocks so that each block has uniform complexity, regardless of the regional variation in complexity. The INR neural network then learns the image information of each block with uniform complexity. By alleviating differences in regional complexity, the neural network is able to learn regional information more stably and accurately, enabling optimal performance even in areas with diverse levels of complexity.

목차

Abstract
1. Introduction
2. Implicit Neural Representation based Signal Processing
3. Proposed Method
4. Experimental Results
5. Conclusion
Acknowledgement
References

키워드

저자

• HoonJae Lee [ Professor, Dept. Information Security, Dongseo University, Korea ]
• Young Sil Lee [ Professor, Dept. Computer Science, International College, Dongseo University, Korea ]
• Suk-Ho Lee [ Professor, Dept. Computer Engineering, Dongseo University, Korea ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  The International Journal of Advanced Smart Convergence

• 간기

  계간

• pISSN

  2288-2847

• eISSN

  2288-2855

• 수록기간

  2012~2025

• 십진분류

  KDC 326 DDC 380