...
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
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
논문 유사도에 따라 DBpia 가 추천하는 논문입니다. 함께 보면 좋을 연관 논문을 확인해보세요!
Deep Learning Method for Extending Image Intensity Using Hybrid Log-Gamma
IEIE Transactions on Smart Processing & Computing
2020.08
Development of a Place Image Analysis Algorithm through Image Crawling and Deep-Learning
한국통신학회 학술대회논문집
2020.11
Tone-mapped High Dynamic Range Image Restoration with a Mask-applied Neural Network
IEIE Transactions on Smart Processing & Computing
2019.04
딥러닝 성능 향상을 위한 3D 의료 영상 증강법
한국CDE학회 논문집
2021.06
Image Denoising Method based on Deep Learning using Improved U-net
IEIE Transactions on Smart Processing & Computing
2021.08
0
