NTIRE 2020 Challenge on Real Image Denoising: Dataset, Methods and Results

Autor: Abdelhamed, Abdelrahman, Afifi, Mahmoud, Timofte, Radu, Brown, Michael S., Cao, Yue, Zhang, Zhilu, Zuo, Wangmeng, Zhang, Xiaoling, Liu, Jiye, Chen, Wendong, Wen, Changyuan, Liu, Meng, Lv, Shuailin, Zhang, Yunchao, Pan, Zhihong, Li, Baopu, Xi, Teng, Fan, Yanwen, Yu, Xiyu, Zhang, Gang, Liu, Jingtuo, Han, Junyu, Ding, Errui, Yu, Songhyun, Park, Bumjun, Jeong, Jechang, Liu, Shuai, Zong, Ziyao, Nan, Nan, Li, Chenghua, Yang, Zengli, Bao, Long, Wang, Shuangquan, Bai, Dongwoon, Lee, Jungwon, Kim, Youngjung, Rho, Kyeongha, Shin, Changyeop, Kim, Sungho, Tang, Pengliang, Zhao, Yiyun, Zhou, Yuqian, Fan, Yuchen, Huang, Thomas, Li, Zhihao, Shah, Nisarg A., Liu, Wei, Yan, Qiong, Zhao, Yuzhi, Możejko, Marcin, Latkowski, Tomasz, Treszczotko, Lukasz, Szafraniuk, Michał, Trojanowski, Krzysztof, Wu, Yanhong, Michelini, Pablo Navarrete, Hu, Fengshuo, Lu, Yunhua, Kim, Sujin, Kim, Wonjin, Lee, Jaayeon, Choi, Jang-Hwan, Zhussip, Magauiya, Khassenov, Azamat, Kim, Jong Hyun, Cho, Hwechul, Kansal, Priya, Nathan, Sabari, Ye, Zhangyu, Lu, Xiwen, Wu, Yaqi, Yang, Jiangxin, Cao, Yanlong, Tang, Siliang, Cao, Yanpeng, Maggioni, Matteo, Marras, Ioannis, Tanay, Thomas, Slabaugh, Gregory, Yan, Youliang, Kang, Myungjoo, Choi, Han-Soo, Song, Kyungmin, Xu, Shusong, Lu, Xiaomu, Wang, Tingniao, Lei, Chunxia, Liu, Bin, Gupta, Rajat, Kumar, Vineet

This paper reviews the NTIRE 2020 challenge on real image denoising with focus on the newly introduced dataset, the proposed methods and their results. The challenge is a new version of the previous NTIRE 2019 challenge on real image denoising that w

Externí odkaz: http://arxiv.org/abs/2005.04117

Superkernel Neural Architecture Search for Image Denoising

Autor: Możejko, Marcin, Latkowski, Tomasz, Treszczotko, Łukasz, Szafraniuk, Michał, Trojanowski, Krzysztof

Recent advancements in Neural Architecture Search(NAS) resulted in finding new state-of-the-art Artificial Neural Network (ANN) solutions for tasks like image classification, object detection, or semantic segmentation without substantial human superv

Externí odkaz: http://arxiv.org/abs/2004.08870

Investigating performance of neural networks and gradient boosting models approximating microscopic traffic simulations in traffic optimization tasks

Autor: Gora, Paweł, Brzeski, Maciej, Możejko, Marcin, Klemenko, Arkadiusz, Kochański, Adrian

We analyze the accuracy of traffic simulations metamodels based on neural networks and gradient boosting models (LightGBM), applied to traffic optimization as fitness functions of genetic algorithms. Our metamodels approximate outcomes of traffic sim

Externí odkaz: http://arxiv.org/abs/1812.00401

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