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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.8 No.2
- 발행연도
- 2019.4
- 수록면
- 121 - 125 (5page)
- DOI
- 10.5573/IEIESPC.2019.8.2.121
이용수
초록· 키워드
오류제보하기
In order to maximize the image quality when using existing image contents in the latest display devices, it is necessary to improve the resolution and intensity of the images. In this paper, we propose an efficient deep neural network to restore the image intensity when there are too few bits per pixel to provide more intensity. We investigate an efficient implementation and training method for U-net to maximize the performance of restoring image intensity. We show that we can significantly improve the perceptual quality of the restored image by using VGG loss as well as MSE loss to train U-net. The perceptual loss of images can be efficiently dealt with by using VGG loss. The convergence of the proposed method is analyzed, and extensive computer simulations show that the proposed method significantly improves the perceptual quality of the restored image.
목차
Abstract
1. Introduction
2. Restoration of Image Intensity and Deep Learning
3. Proposed Method for Restoring Image Intensity Based on Deep Learning
4. Performance Evaluation
5. Conclusion
References
참고문헌 (15)
참고문헌 신청
C. Dong / 2016 / Image super-resolution using deep convolutional networks / IEEE Transactions on Pattern Analysis and Machine Intelligence 38(2):295~307
J. Johnson / 2016 / Perceptual losses for real-time style transfer and superresolution / Proc. of European Conference on Computer Vision 2016 :694~711
C. Ledig / 2017 / Photo-realistic single image super-resolution using a generative adversarial network / Proc. of IEEE Conference on Computer Vision and Pattern Recognition 2017 :105~114
G. Eilertsen / 2017 / HDR image reconstruction from a single exposure using deep CNNs / ACM Transactions on Graphics 36(6)
R. P. Kovaleski / 2014 / High-quality reverse tone mapping for a wide range of exposures / Proc. of 27th SIBGRAPI Conference on Graphics, Patterns and Images
J. Johnson / 2016 / Perceptual losses for real-time style transfer and superresolution / Proc. of European Conference on Computer Vision 2016 :694~711
C. Ledig / 2017 / Photo-realistic single image super-resolution using a generative adversarial network / Proc. of IEEE Conference on Computer Vision and Pattern Recognition 2017 :105~114
G. Eilertsen / 2017 / HDR image reconstruction from a single exposure using deep CNNs / ACM Transactions on Graphics 36(6)
R. P. Kovaleski / 2014 / High-quality reverse tone mapping for a wide range of exposures / Proc. of 27th SIBGRAPI Conference on Graphics, Patterns and Images
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