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논문검색

Human-Machine Interaction Technology (HIT)

Joint Demosaicing and Super-resolution of Color Filter Array Image based on Deep Image Prior Network

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

저자정보

  • Edwin Kurniawan PhD Candidate, Dept. Computer Engineering, Dongseo University, Korea
  • Suk-Ho Lee Professor, Dept. Computer Engineering, Dongseo University, Korea

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