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BIGPrior: Toward Decoupling Learned Prior Hallucination and Data Fidelity in Image Restoration
Classic image-restoration algorithms use a variety of priors, either implicitly or explicitly. Their priors are hand-designed and their corresponding weights are heuristically assigned. Hence, deep learning methods often produce superior image restoration quality. Deep networks are, however, capable of inducing strong and hardly predictable hallucinations. Networks implicitly learn to be jointly f...Show More
NTIRE 2021 Depth Guided Image Relighting Challenge
Majed El Helou;Ruofan Zhou;Sabine Süsstrunk;Radu Timofte;Maitreya Suin;A. N. Rajagopalan;Yuanzhi Wang;Tao Lu;Yanduo Zhang;Yuntao Wu;Hao-Hsiang Yang;Wei-Ting Chen;Sy-Yen Kuo;Hao-Lun Luo;Zhiguang Zhang;Zhipeng Luo;Jianye He;Zuo-Liang Zhu;Zhen Li;Jia-Xiong Qiu;Zeng-Sheng Kuang;Cheng-Ze Lu;Ming-Ming Cheng;Xiu-Li Shao;Chenghua Li;Bosong Ding;Wanli Qian;Fangya Li;Fu Li;Ruifeng Deng;Tianwei Lin;Songhua Liu;Xin Li;Dongliang He;Amirsaeed Yazdani;Tiantong Guo;Vishal Monga;Ntumba Elie Nsampi;Zhongyun Hu;Qing Wang;Sabari Nathan;Priya Kansal;Tongtong Zhao;Shanshan Zhao
2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
Year: 2021 | Conference Paper |
Cited by: Papers (19)
Image relighting is attracting increasing interest due to its various applications. From a research perspective, im-age relighting can be exploited to conduct both image normalization for domain adaptation, and also for data augmentation. It also has multiple direct uses for photo montage and aesthetic enhancement. In this paper, we review the NTIRE 2021 depth guided image relighting challenge.We ...Show More
NTIRE 2021 Depth Guided Image Relighting Challenge
Majed El Helou;Ruofan Zhou;Sabine Süsstrunk;Radu Timofte;Maitreya Suin;A. N. Rajagopalan;Yuanzhi Wang;Tao Lu;Yanduo Zhang;Yuntao Wu;Hao-Hsiang Yang;Wei-Ting Chen;Sy-Yen Kuo;Hao-Lun Luo;Zhiguang Zhang;Zhipeng Luo;Jianye He;Zuo-Liang Zhu;Zhen Li;Jia-Xiong Qiu;Zeng-Sheng Kuang;Cheng-Ze Lu;Ming-Ming Cheng;Xiu-Li Shao;Chenghua Li;Bosong Ding;Wanli Qian;Fangya Li;Fu Li;Ruifeng Deng;Tianwei Lin;Songhua Liu;Xin Li;Dongliang He;Amirsaeed Yazdani;Tiantong Guo;Vishal Monga;Ntumba Elie Nsampi;Zhongyun Hu;Qing Wang;Sabari Nathan;Priya Kansal;Tongtong Zhao;Shanshan Zhao
Year: 2021 | Conference Paper |
Fidelity Estimation Improves Noisy-Image Classification With Pretrained Networks
Xiaoyu Lin;Deblina Bhattacharjee;Majed El Helou;Sabine Süsstrunk
Image classification has significantly improved using deep learning. This is mainly due to convolutional neural networks (CNNs) that are capable of learning rich feature extractors from large datasets. However, most deep learning classification methods are trained on clean images and are not robust when handling noisy ones, even if a restoration preprocessing step is applied. While novel methods a...Show More
Realizability of Planar Point Embeddings from Angle Measurements
ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Year: 2020 | Conference Paper |
Cited by: Papers (1)
Localization of a set of nodes is an important and a thoroughly researched problem in robotics and sensor networks. This paper is concerned with the theory of localization from inner-angle measurements. We focus on the challenging case where no anchor locations are known.Inspired by Euclidean distance matrices, we investigate when a set of inner angles corresponds to a realizable point set. In par...Show More
Divergence-Based Adaptive Extreme Video Completion
Majed El Helou;Ruofan Zhou;Frank Schmutz;Fabrice Guibert;Sabine Süsstrunk
ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Year: 2020 | Conference Paper |
Extreme image or video completion, where, for instance, we only retain 1% of pixels in random locations, allows for very cheap sampling in terms of the required pre-processing. The consequence is, however, a reconstruction that is challenging for humans and inpainting algorithms alike. We propose an extension of a state-of-the-art extreme image completion algorithm to extreme video completion. We ...Show More
Blind Universal Bayesian Image Denoising With Gaussian Noise Level Learning
Blind and universal image denoising consists of using a unique model that denoises images with any level of noise. It is especially practical as noise levels do not need to be known when the model is developed or at test time. We propose a theoretically-grounded blind and universal deep learning image denoiser for additive Gaussian noise removal. Our network is based on an optimal denoising soluti...Show More
NTIRE 2018 Challenge on Spectral Reconstruction from RGB Images
Boaz Arad;Ohad Ben-Shahar;Radu Timofte;Luc Van Gool;Lei Zhang;Ming-Hsuan Yang;Zhiwei Xiong;Chang Chen;Zhan Shi;Dong Liu;Feng Wu;Charis Lanaras;Silvano Galliani;Konrad Schindler;Tarek Stiebel;Simon Koppers;Philipp Seltsam;Ruofan Zhou;Majed El Helou;Fayez Lahoud;Marjan Shahpaski;Ke Zheng;Lianru Gao;Bing Zhang;Ximin Cui;Haoyang Yu;Yigit Baran Can;Aitor Alvarez-Gila;Joost van de Weijer;Estibaliz Garrote;Adrian Galdran;Manoj Sharma;Sriharsha Koundinya;Avinash Upadhyay;Raunak Manekar;Rudrabha Mukhopadhyay;Himanshu Sharma;Santanu Chaudhury;Koushik Nagasubramanian;Sambuddha Ghosal;Asheesh K. Singh;Arti Singh;Baskar Ganapathysubramanian;Soumik Sarkar
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
Year: 2018 | Conference Paper |
Cited by: Papers (73)
This paper reviews the first challenge on spectral image reconstruction from RGB images, i.e., the recovery of whole-scene hyperspectral (HS) information from a 3-channel RGB image. The challenge was divided into 2 tracks: the "Clean" track sought HS recovery from noiseless RGB images obtained from a known response function (representing spectrally-calibrated camera) while the "Real World" track c...Show More
NTIRE 2018 Challenge on Spectral Reconstruction from RGB Images
Boaz Arad;Ohad Ben-Shahar;Radu Timofte;Luc Van Gool;Lei Zhang;Ming-Hsuan Yang;Zhiwei Xiong;Chang Chen;Zhan Shi;Dong Liu;Feng Wu;Charis Lanaras;Silvano Galliani;Konrad Schindler;Tarek Stiebel;Simon Koppers;Philipp Seltsam;Ruofan Zhou;Majed El Helou;Fayez Lahoud;Marjan Shahpaski;Ke Zheng;Lianru Gao;Bing Zhang;Ximin Cui;Haoyang Yu;Yigit Baran Can;Aitor Alvarez-Gila;Joost van de Weijer;Estibaliz Garrote;Adrian Galdran;Manoj Sharma;Sriharsha Koundinya;Avinash Upadhyay;Raunak Manekar;Rudrabha Mukhopadhyay;Himanshu Sharma;Santanu Chaudhury;Koushik Nagasubramanian;Sambuddha Ghosal;Asheesh K. Singh;Arti Singh;Baskar Ganapathysubramanian;Soumik Sarkar
Year: 2018 | Conference Paper |
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