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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 대한전자공학회 IEIE Transactions on Smart Processing & Computing IEIE Transactions on Smart Processing & Computing Vol.10 No.2
- 발행연도
- 2021.4
- 수록면
- 96 - 100 (5page)
- DOI
- 10.5573/IEIESPC.2021.10.2.096
이용수
초록· 키워드
오류제보하기
Deep learning using a convolutional neural network has become a state-of-art technique in image processing. In recent scenarios, image denoising using a residual image in deep learning has been popular. However, one aspect missing in these methods is that the residual image has all the noise and very small structured details of the input image. Therefore, we have developed a Gaussian filter residual convolutional neural network architecture for color image denoising. Gaussian residual learning was used to boost the denoising performance. The architecture is designed to remove additive white Gaussian noise, which is one of the most basic types of noise that affects an image when captured. The network with Gaussian residual learning removes the clean image using the features learned from the hidden layer. The peak signal-to-noise ratio and structural similarity index measure achieved by our method reveals that the presented approach is better at denoising images with Gaussian noise than a convolutional neural network.
목차
Abstract
1. Introduction
2. Background
3. The Proposed Scheme
4. Results and Discussion
5. Conclusion
References
참고문헌 (19)
참고문헌 신청
H. Talebi, X. Zhu, and P. Milanfar, “How to SAIF-ly boost denoising performance,” IEEE Trans. Image Process., Vol. 22, No. 4, pp. 1470-1485, 2013. Article (CrossRef Link)
G. Varghese, and Z. Wang, “Video denoising based on a spatiotemporal Gaussian scale mixture model,” IEEE Trans. Circuits Syst. Video Technol., Vol. 20, No. 7, pp. 1032-1040, 2010. Article (CrossRef Link)
M. K. Ozkan, A. T. Erdem, M. I. Sezan, and A. M. Tekalp, “Efficient multiframe Wiener restoration of blurred and noisy image sequences,” IEEE Trans. Image Process., Vol. 1, pp. 453-476, 1992. Article (CrossRef Link)
A. Rosenfeld, and A. C. Kak, “Digital picture processing,” Second edition, Academic, New York, USA, 1982.
J. J. Hwang, and K. H. Rhee, “Gaussian filtering detection based on features of residuals in image forensics,” in Proc. of IEEE Int. Conf. Computing & Communication Technologies, Research, Innovation, and Vision for the Future, Hanoi, Vietnam, Nov. 2016, pp. 153-157, Article (CrossRef Link)
G. Varghese, and Z. Wang, “Video denoising based on a spatiotemporal Gaussian scale mixture model,” IEEE Trans. Circuits Syst. Video Technol., Vol. 20, No. 7, pp. 1032-1040, 2010. Article (CrossRef Link)
M. K. Ozkan, A. T. Erdem, M. I. Sezan, and A. M. Tekalp, “Efficient multiframe Wiener restoration of blurred and noisy image sequences,” IEEE Trans. Image Process., Vol. 1, pp. 453-476, 1992. Article (CrossRef Link)
A. Rosenfeld, and A. C. Kak, “Digital picture processing,” Second edition, Academic, New York, USA, 1982.
J. J. Hwang, and K. H. Rhee, “Gaussian filtering detection based on features of residuals in image forensics,” in Proc. of IEEE Int. Conf. Computing & Communication Technologies, Research, Innovation, and Vision for the Future, Hanoi, Vietnam, Nov. 2016, pp. 153-157, Article (CrossRef Link)
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