T
Tiantong Guo
Researcher at Pennsylvania State University
Publications - 32
Citations - 2018
Tiantong Guo is an academic researcher from Pennsylvania State University. The author has contributed to research in topics: Deep learning & Convolutional neural network. The author has an hindex of 13, co-authored 32 publications receiving 1312 citations. Previous affiliations of Tiantong Guo include Politehnica University of Timișoara.
Papers
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Proceedings ArticleDOI
NTIRE 2017 Challenge on Single Image Super-Resolution: Methods and Results
Radu Timofte,Eirikur Agustsson,Luc Van Gool,Ming-Hsuan Yang,Lei Zhang,Bee Oh Lim,Sanghyun Son,Heewon Kim,Seungjun Nah,Kyoung Mu Lee,Xintao Wang,Yapeng Tian,Ke Yu,Yulun Zhang,Shixiang Wu,Chao Dong,Liang Lin,Yu Qiao,Chen Change Loy,Woong Bae,Jaejun Yoo,Yoseob Han,Jong Chul Ye,Jae-Seok Choi,Munchurl Kim,Yuchen Fan,Jiahui Yu,Wei Han,Ding Liu,Haichao Yu,Zhangyang Wang,Honghui Shi,Xinchao Wang,Thomas S. Huang,Yunjin Chen,Kai Zhang,Wangmeng Zuo,Zhimin Tang,Linkai Luo,Shaohui Li,Min Fu,Lei Cao,Wen Heng,Giang Bui,Truc Le,Ye Duan,Dacheng Tao,Ruxin Wang,Xu Lin,Jianxin Pang,Xu Jinchang,Yu Zhao,Xiangyu Xu,Jinshan Pan,Deqing Sun,Yujin Zhang,Xibin Song,Yuchao Dai,Xueying Qin,Xuan-Phung Huynh,Tiantong Guo,Hojjat Seyed Mousavi,Tiep H. Vu,Vishal Monga,Cristóvão Cruz,Karen Egiazarian,Vladimir Katkovnik,Rakesh Mehta,Arnav Kumar Jain,Abhinav Agarwalla,Ch V. Sai Praveen,Ruofan Zhou,Hongdiao Wen,Che Zhu,Zhiqiang Xia,Zhengtao Wang,Qi Guo +76 more
TL;DR: This paper reviews the first challenge on single image super-resolution (restoration of rich details in an low resolution image) with focus on proposed solutions and results and gauges the state-of-the-art in single imagesuper-resolution.
Proceedings ArticleDOI
Deep Wavelet Prediction for Image Super-Resolution
TL;DR: This work designs a deep CNN to predict the "missing details" of wavelet coefficients of the low-resolution images to obtain the Super-Resolution (SR) results, which it shows is computationally simpler and yet produces competitive and often better results than state-of-the-art alternatives.
Proceedings ArticleDOI
NTIRE 2020 challenge on nonhomogeneous dehazing
Codruta O. Ancuti,Cosmin Ancuti,Florin-Alexandru Vasluianu,Radu Timofte,Jing Liu,Haiyan Wu,Yuan Xie,Yanyun Qu,Lizhuang Ma,Ziling Huang,Qili Deng,Ju-Chin Chao,Tsung-Shan Yang,Peng-Wen Chen,Po-Min Hsu,Tzu-Yi Liao,Chung-En Sun,Pei-Yuan Wu,Jeonghyeok Do,Jongmin Park,Munchurl Kim,Kareem Metwaly,Xuelu Li,Tiantong Guo,Vishal Monga,Mingzhao Yu,Venkateswararao Cherukuri,Shiue-Yuan Chuang,Tsung-Nan Lin,David Lee,Jerome Chang,Zhan-Han Wang,Yu-Bang Chang,Chang-Hong Lin,Yu Dong,Hongyu Zhou,Xiangzhen Kong,Sourya Dipta Das,Saikat Dutta,Xuan Zhao,Bing Ouyang,Dennis Estrada,Meiqi Wang,Tianqi Su,Siyi Chen,Bangyong Sun,Vincent Jacob Whannou de Dravo,Zhe Yu,Pratik Narang,Aryan Mehra,Navaneeth Raghunath,Murari Mandal +51 more
TL;DR: This paper reviews the NTIRE 2020 Challenge on Non-Homogeneous Dehazing of images (restoration of rich details in hazy image) and proposed solutions gauge the state-of-the-art in image dehazing.
Journal ArticleDOI
Prior Information Guided Regularized Deep Learning for Cell Nucleus Detection
TL;DR: New network structures that can incorporate “expected behavior” of nucleus shapes via two components: learnable layers that perform the nucleus detection and a fixed processing part that guides the learning with prior information are presented.
Proceedings ArticleDOI
Dense Scene Information Estimation Network for Dehazing
TL;DR: Two novel network architectures, denoted as At-DH and AtJ-DH, which can outperform state-of-the-art alternatives, especially when recovering images corrupted by dense haze are demonstrated.