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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.9 No.2
- 발행연도
- 2020.4
- 수록면
- 135 - 141 (7page)
- DOI
- 10.5573/IEIESPC.2020.9.2.135
이용수
초록· 키워드
오류제보하기
In today’s scenarios, deep learning has fascinated all researchers from numerous arenas who developed ways to achieve obligatory outcomes. In deep learning, transfer learning is undergoing deep study, because the study helps to practice a pre-trained network for our own tasks. A novel, transfer-learned AlexNet-based residual learning for Gaussian noise reduction is presented in this paper. The method can remove any level of Gaussian noise without having information about the noise variance in both gray scale and color images. Therefore, our technique is blind Gaussian image denoising that learns a residual image by eradicating the clean image from the transfer-learned AlexNet, and removes noise by identifying the difference from the input image. Experimental results with the proposed scheme are compared against a Gaussian denoiser for image denoising in terms of peak signal-to-noise ratio (PSNR) and visual perception. The results have revealed that our residual learning using transfer-learned AlexNet attains promising denoising results.
목차
Abstract
1. Introduction
2. Related Work
3. Transfer-learned AlexNet Architecture for Residual Learning
4. Experimental Results
5. Conclusion
References
참고문헌 (26)
참고문헌 신청
H. Andrews / 1976 / Singular Value Decompositions and Digital Image Processing / IEEE Transactions on Acoustics, Speech, Signal Processing 24(1):26~53
S. M. Smith / 1997 / SUSAN-A new approach to low level image processing / International Journal of Computer Vision 23(1):45~78
C. Tomasi / 1998 / Bilateral Filtering for Gray and Color Images / Proc. of 6th IEEE International Conference on Computer Vision :839~846
L. Sendur / 2002 / Bivariate Shrinkage Functions for Wavelet-based Denoising Exploiting Interscale Dependency / IEEE Transactions on Signal Processing 50(11):2744~2756
L. Sendur / 2002 / Bivariate Shrinkage with Local Variance Estimation / IEEE Signal Processing Letters 9(12):438~441
S. M. Smith / 1997 / SUSAN-A new approach to low level image processing / International Journal of Computer Vision 23(1):45~78
C. Tomasi / 1998 / Bilateral Filtering for Gray and Color Images / Proc. of 6th IEEE International Conference on Computer Vision :839~846
L. Sendur / 2002 / Bivariate Shrinkage Functions for Wavelet-based Denoising Exploiting Interscale Dependency / IEEE Transactions on Signal Processing 50(11):2744~2756
L. Sendur / 2002 / Bivariate Shrinkage with Local Variance Estimation / IEEE Signal Processing Letters 9(12):438~441
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