Zobrazeno 1 - 9
of 9
pro vyhledávání: '"Wu, Renlong"'
Despite the significant progress in image denoising, it is still challenging to restore fine-scale details while removing noise, especially in extremely low-light environments. Leveraging near-infrared (NIR) images to assist visible RGB image denoisi
Externí odkaz:
http://arxiv.org/abs/2404.08514
When zooming between dual cameras on a mobile, noticeable jumps in geometric content and image color occur in the preview, inevitably affecting the user's zoom experience. In this work, we introduce a new task, ie, dual-camera smooth zoom (DCSZ) to a
Externí odkaz:
http://arxiv.org/abs/2404.04908
Autor:
Wu, Renlong, Zhang, Zhilu, Zhang, Shuohao, Gou, Longfei, Chen, Haobin, Zhang, Lei, Chen, Hao, Zuo, Wangmeng
Due to the difficulty of collecting real paired data, most existing desmoking methods train the models by synthesizing smoke, generalizing poorly to real surgical scenarios. Although a few works have explored single-image real-world desmoking in unpa
Externí odkaz:
http://arxiv.org/abs/2403.11192
It is highly desired but challenging to acquire high-quality photos with clear content in low-light environments. Although multi-image processing methods (using burst, dual-exposure, or multi-exposure images) have made significant progress in address
Externí odkaz:
http://arxiv.org/abs/2401.00766
Burst super-resolution (BurstSR) aims at reconstructing a high-resolution (HR) image from a sequence of low-resolution (LR) and noisy images, which is conducive to enhancing the imaging effects of smartphones with limited sensors. The main challenge
Externí odkaz:
http://arxiv.org/abs/2306.17595
Autor:
Yang, Ren, Timofte, Radu, Zheng, Meisong, Xing, Qunliang, Qiao, Minglang, Xu, Mai, Jiang, Lai, Liu, Huaida, Chen, Ying, Ben, Youcheng, Zhou, Xiao, Fu, Chen, Cheng, Pei, Yu, Gang, Li, Junyi, Wu, Renlong, Zhang, Zhilu, Shang, Wei, Lv, Zhengyao, Chen, Yunjin, Zhou, Mingcai, Ren, Dongwei, Zhang, Kai, Zuo, Wangmeng, Ostyakov, Pavel, Dmitry, Vyal, Soltanayev, Shakarim, Sergey, Chervontsev, Magauiya, Zhussip, Zou, Xueyi, Yan, Youliang, Michelini, Pablo Navarrete, Lu, Yunhua, Zhang, Diankai, Liu, Shaoli, Gao, Si, Wu, Biao, Zheng, Chengjian, Zhang, Xiaofeng, Lu, Kaidi, Wang, Ning, Canh, Thuong Nguyen, Bach, Thong, Wang, Qing, Sun, Xiaopeng, Ma, Haoyu, Zhao, Shijie, Li, Junlin, Xie, Liangbin, Shi, Shuwei, Yang, Yujiu, Wang, Xintao, Gu, Jinjin, Dong, Chao, Shi, Xiaodi, Nian, Chunmei, Jiang, Dong, Lin, Jucai, Xie, Zhihuai, Ye, Mao, Luo, Dengyan, Peng, Liuhan, Chen, Shengjie, Liu, Xin, Wang, Qian, Liang, Boyang, Dong, Hang, Huang, Yuhao, Chen, Kai, Guo, Xingbei, Sun, Yujing, Wu, Huilei, Wei, Pengxu, Huang, Yulin, Chen, Junying, Lee, Ik Hyun, Khowaja, Sunder Ali, Yoon, Jiseok
This paper reviews the NTIRE 2022 Challenge on Super-Resolution and Quality Enhancement of Compressed Video. In this challenge, we proposed the LDV 2.0 dataset, which includes the LDV dataset (240 videos) and 95 additional videos. This challenge incl
Externí odkaz:
http://arxiv.org/abs/2204.09314
Akademický článek
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Publikováno v:
2011 International Conference on Electronics, Communications & Control (ICECC); 2011, p1533-1536, 4p
Autor:
Jin, Youxin, Qiu, Musui, Li, Wenqin, Zeng, Keqin, Bao, Junru, Gong, Peijuan, Wu, Renlong, Wang (T.P. Wang), Debao
Publikováno v:
In Analytical Biochemistry 1987 161(2):453-459