Workshop Program
November 2 (Saturday), 2019, Room 327BC , COEX.
- 8:30-8:40: Opening and Welcome
- 8:40-9:20: Invited talk by Orazio Gallo. Title: A Few Learnings on Computational Photography with Deep Learning
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- 9:20-9:30: 10-min Break
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- 10:50-11:00: 10-min Break
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- 11:00-12:20: Contributed Oral Presentations:
- DeepRED: Deep Image Prior Powered by RED Gary Mataev, Michael Elad, and Peyman Milanfar. [ Paper ]
- Lightweight and Accurate Recursive Fractal Network for Image Super-Resolution Juncheng Li, Yiting Yuan, Kangfu Mei, and Faming Fang. [ Paper ]
- Deep Video Deblurring: The Devil is in the Details Jochen Gast, and Stefan Roth. [ Paper ]
- Direct Reconstruction from Sinogram Data using Stacked Back Projection Wenrui Li, Gregery T. Buzzard, and Charles A. Bouman.
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12:20-13:30: Lunch
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13:30-14:10: Invited Talk by Jong Chul Ye. Title: Geometry of deep learning for inverse problems
14:10-14:50: Invited Talk by Leslie Ying. Title: Machine Learning in Biomedical Image Reconstruction [Slides]
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14:50-15:00: 10-min Break
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15:00-16:00: Contributed Oral Presentations:
- Blind Unitary Transform Learning for Inverse Problems in Light-field Imaging Cameron J Blocker, and Jeffrey Fessler. [ Paper ]
- Integrating Data and Image Domain Deep Learning for Limited Angle Tomography using Consensus Equilibrium Muhammad Usman Ghani, and Clem Karl. [ Paper ]
- SUPER Learning: A Supervised-Unsupervised Framework for Low-Dose CT Image Reconstruction Zhipeng Li, Siqi Ye, Yong Long, and Saiprasad Ravishankar. [ Paper ]
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16:00-16:20: 20-min break to move to poster session
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16:20-18:00: Poster Session (23 posters):
- CNN-based Cross-dataset No-reference Image Quality Assessment Dan Yang, Veli-Tapani Peltoketo, and Joni-Kristian Kamarainen. (Poster ID = 117 )
- Flickr1024: A Large-Scale Dataset for Stereo Image Super-Resolution Yingqian Wang, Longguang Wang, Jungang Yang, Wei An, and Yulan Guo. (Poster ID = 118 )
- A HVS-inspired Attention to Improve Loss Metrics for CNN-based Perception-Oriented Super-Resolution Taimoor Tariq, Juan Luis Gonzalez, and Munchurl Kim. (Poster ID = 119 )
- Semi-supervised Eye Makeup Transfer by Swapping Learned Representation Feida Zhu, Hongji Cao, Zunlei Feng, Zhang Yongqiang, Luo Wenbin, Hucheng Zhou, Mingli Song, and Kai-Kuang Ma. (Poster ID = 120 )
- Deep Camera: A Fully Convolutional Neural Network for Image Signal Processing Sivalogeswaran Ratnasingam. (Poster ID = 121 )
- Removing Imaging Artifacts in Electron Microscopy using an Asymmetrically Cyclic Adversarial Network without Paired Training Data Tran Minh Quan, David Hildebrand, Kanggeun Lee, Logan Thomas, Aaron Kuan, Wei-chung Lee, and Won-Ki Jeong. (Poster ID = 122 ) [ Paper ]
- A Simple and Robust Deep Convolutional Approach to Blind Image Denoising Hengyuan Zhao, Wenze Shao, Bingkun Bao, and Haibo Li. (Poster ID = 123 )
- RIDNet: Recursive Information Distillation Network for Color Image Denoising Shengkai Zhuo, Zhi Jin, Wenbin Zou, and Xia Li. (Poster ID = 124 )
- Deep learning-based imaging using single-lens and multi-aperture diffractive optical systems Viktoriia Evdokimova, Roman Skidanov, Maxim Petrov, Sergey Bibikov, Pavel Yakimov, Yuriy Yuzifovich, Nikolay Kazansky, and Artem Nikonorov. (Poster ID = 125 )
- Deep Hyperspectral Prior: Single-Image Denoising, Inpainting, Super-Resolution Oleksii Sidorov, and Jon Yngve Hardeberg. (Poster ID = 126 )
- Deep Reinforcement Learning Designed RF: DeepRFSLR Dongmyung Shin, Sooyeon Ji, Doohee Lee, Jieun Lee, Se-Hong Oh, and Jongho Lee. (Poster ID = 127 )
- Deep Compressive Sensing for Visual Privacy Protection Thuong Nguyen Canh, and Hajime Nagahara. (Poster ID = 128 )
- Deep Plug-and-Play Prior for Parallel MRI Reconstruction Ali Pour Yazdanpanah. (Poster ID = 129 )
- Adaptive Ptych: Leveraging Image Adaptive Generative Priors for Subsampled Fourier Ptychography Fahad Shamshad, Asif Hanif, Farwa Abbas, Muhammad Awais, and Ali Ahmed. (Poster ID = 130 )
- Multispectral Denoising for Satellite Imaging Sensors using Wavelet Domain Deep Learning Joonyoung Song, and Jong Chul Ye. (Poster ID = 131 )
- Image Super-resolution via Residual Block Attention Networks Tao Dai, Hua Zha, Shutao Xia, and Yong Jiang. (Poster ID = 132 )
- Deep learning for dense single-molecule localization microscopy Elias Nehme, Tomer Michaeli, Yoav Shechtman, and Daniel Freedman. (Poster ID = 133 )
- Unsupervised learning of denoisers with compressive sensing measurements Magauiya Zhussip, Shakarim Soltanayev, and Se Young Chun. (Poster ID = 134 )
- Exploring the linearity of deep neural network trained quantitative susceptibility mapping: QSMnet+ Woojin Jung, Jaeyeon Yoon, Eung-Yeop Kim, Yoonho Nam, and Jongho Lee. (Poster ID = 135 )
- Two-layer Residual Sparsifying Transform Learning for Image Reconstruction Xuehang Zheng, Saiprasad Ravishankar, Yong Long, Marc Louis Klasky, and Brendt Wohlberg. (Poster ID = 136 )
- Annotation-free segmentation of neuronal cell bodies from a large-scale 3D neuron image using deep neural networks Hyoungjun Park, Myeongsu Na, Sunghoe Chang, and Jong Chul Ye. (Poster ID = 137 )
- Snapshot Compressive Imaging: Theory, Algorithms and Applications Xin Yuan. (Poster ID = 138 )
- Formulating Camera-Adaptive Color Constancy as a Few-shot Meta-Learning Problem Steven McDonagh, Sarah Parisot, Fengwei Zhou, Xing Zhang, Ales Leonardis, Zhenguo Li, and Gregory Slabaugh. (Poster ID = 139 )
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18:00: Closing of the LCI Workshop
ICCV LCI Workshop paper:
All of the regular papers are available online, check the full paper list.
Arrangement for the poster session:
- The poster presentation will be in the afternoon, from 4:20pm - 6pm. The earliest time that you can put up your poster is at 4pm.
- Our posters session will be located at COEX 3F, Secion 3 and Section 4. Please find the exact locations according to the following map.
- You can find your poster ID in the program (poster session) now.
- ID = 117 - 120 will be in Section 3, and ID = 121 - 139 will be in Section 4. The two sections are next to each other.