Zobrazeno 1 - 10
of 30
pro vyhledávání: '"Canh, Thuong Nguyen"'
Recent advancements in deep learning techniques have significantly improved the quality of compressed videos. However, previous approaches have not fully exploited the motion characteristics of compressed videos, such as the drastic change in motion
Externí odkaz:
http://arxiv.org/abs/2302.13594
Lensless imaging protects visual privacy by capturing heavily blurred images that are imperceptible for humans to recognize the subject but contain enough information for machines to infer information. Unfortunately, protecting visual privacy comes w
Externí odkaz:
http://arxiv.org/abs/2302.02255
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
Autor:
Canh, Thuong Nguyen, Jeon, Byeungwoo
Recently, deep learning-based compressive imaging (DCI) has surpassed the conventional compressive imaging in reconstruction quality and faster running time. While multi-scale has shown superior performance over single-scale, research in DCI has been
Externí odkaz:
http://arxiv.org/abs/2008.00802
Autor:
Canh, Thuong Nguyen, Jeon, Byeungwoo
Compressive sensing (CS) is well-known for its unique functionalities of sensing, compressing, and security (i.e. CS measurements are equally important). However, there is a tradeoff. Improving sensing and compressing efficiency with prior signal inf
Externí odkaz:
http://arxiv.org/abs/2002.07346
This paper considers the sum spectral efficiency (SE) optimization problem in multi-cell Massive MIMO systems with a varying number of active users. This is formulated as a joint pilot and data power control problem. Since the problem is non-convex,
Externí odkaz:
http://arxiv.org/abs/1901.03620
Autor:
Canh, Thuong Nguyen, Jeon, Byeungwoo
With joint learning of sampling and recovery, the deep learning-based compressive sensing (DCS) has shown significant improvement in performance and running time reduction. Its reconstructed image, however, losses high-frequency content especially at
Externí odkaz:
http://arxiv.org/abs/1809.05717
This paper addresses an ill-posed problem of recovering a color image from its compressively sensed measurement data. Differently from the typical 1D vector-based approach of the state-of-the-art methods, we exploit the nonlocal similarities inherent
Externí odkaz:
http://arxiv.org/abs/1711.09375
Autor:
Canh, Thuong Nguyen, Jeon, Byeungwoo
Publikováno v:
In Signal Processing: Image Communication January 2021 90
Publikováno v:
In Signal Processing: Image Communication November 2016 49:63-78