Earticle

초록

영어

In this paper, we propose a learning based joint demosaicing and super-resolution framework which uses only the mosaiced color filter array(CFA) image as the input. As the proposed method works only on the mosaicied CFA image itself, there is no need for a large dataset. Based on our framework, we proposed two different structures, where the first structure uses one deep image prior network, while the second uses two. Experimental results show that even though we use only the CFA image as the training image, the proposed method can result in better visual quality than other bilinear interpolation combined demosaicing methods, and therefore, opens up a new research area for joint demosaicing and super-resolution on raw images.

목차

Abstract
1. Introduction
2. Preliminaries
2.1 Demosaicing problem
2.2 Deep Image Prior Network
3. Proposed Joint Demosaicing and Super-resolution Method
4. Experimental Results
5. Conclusion
Acknowledgement
References

키워드

저자

• Edwin Kurniawan [ PhD Candidate, Dept. Computer Engineering, 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