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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- Korean Institute of Information Scientists and Engineers Journal of Computing Science and Engineering Journal of Computing Science and Engineering Vol.16 No.4
- 발행연도
- 2022.12
- 수록면
- 185 - 193 (9page)
- DOI
- 10.5626/JCSE.2022.16.4.185
이용수
초록· 키워드
오류제보하기
Recently, deep video denoising networks have shown substantially higher denoising performance with considerably lower computing times. However, due to the various characteristics of video motion, such models may not be able to denoise long-term frames. In this paper, we propose a method that takes longer input frames and feeds them to the existing architecture. In particular, the proposed method can effectively extract temporal information from frames that are dependent on each other over a long period of time. To demonstrate the performance of the proposed method, we implemented our method on the state-of-the-art video denoising model. Through the extensive experiments, the proposed method showed better performance in terms of quality metrics than the existing one, even with a higher noise level, resulting in considerably lower computing times.
목차
Abstract
I. INTRODUCTION
II. OVERVIEW OF VIDEO DENOISING MODEL
III. PROPOSED METHOD
IV. EXPERIMENTAL RESULTS
V. CONCLUSION
REFERENCES
참고문헌 (28)
참고문헌 신청
Y. Chen and T. Pock, “Trainable nonlinear reaction diffusion: a flexible framework for fast and effective image restoration,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 39, no. 6, pp. 1256-1272, 2017.
U. Schmidt and S. Roth, “Shrinkage fields for effective image restoration,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, 2014, pp. 2774-2781.
K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, “Image denoising by sparse 3-D transform-domain collaborative filtering,” IEEE Transactions on Image Processing, vol. 16, no. 8, pp. 2080-2095, 2007.
M. Lebrun, A. Buades, and J. M. Morel, “A nonlocal Bayesian image denoising algorithm,” SIAM Journal on Imaging Sciences, vol. 6, no. 3, pp. 1665-1688, 2013.
H. C. Burger, C. J. Schuler, and S. Harmeling, “Image denoising: can plain neural networks compete with BM3D?,” in Proceedings of 2012 IEEE Conference on Computer Vision and Pattern Recognition, Providence, RI, 2012, pp. 2392-2399.
U. Schmidt and S. Roth, “Shrinkage fields for effective image restoration,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, 2014, pp. 2774-2781.
K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, “Image denoising by sparse 3-D transform-domain collaborative filtering,” IEEE Transactions on Image Processing, vol. 16, no. 8, pp. 2080-2095, 2007.
M. Lebrun, A. Buades, and J. M. Morel, “A nonlocal Bayesian image denoising algorithm,” SIAM Journal on Imaging Sciences, vol. 6, no. 3, pp. 1665-1688, 2013.
H. C. Burger, C. J. Schuler, and S. Harmeling, “Image denoising: can plain neural networks compete with BM3D?,” in Proceedings of 2012 IEEE Conference on Computer Vision and Pattern Recognition, Providence, RI, 2012, pp. 2392-2399.
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