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pro vyhledávání: '"Yang, Jungang"'
Differential Privacy (DP) mechanisms, especially in high-dimensional settings, often face the challenge of maintaining privacy without compromising the data utility. This work introduces an innovative shuffling mechanism in Differentially-Private Sto
Externí odkaz:
http://arxiv.org/abs/2407.15414
Autor:
Wang, Xinbing, Fu, Luoyi, Gan, Xiaoying, Wen, Ying, Zheng, Guanjie, Ding, Jiaxin, Xiang, Liyao, Ye, Nanyang, Jin, Meng, Liang, Shiyu, Lu, Bin, Wang, Haiwen, Xu, Yi, Deng, Cheng, Zhang, Shao, Kang, Huquan, Wang, Xingli, Li, Qi, Guo, Zhixin, Qi, Jiexing, Liu, Pan, Ren, Yuyang, Wu, Lyuwen, Yang, Jungang, Zhou, Jianping, Zhou, Chenghu
The exponential growth of scientific literature requires effective management and extraction of valuable insights. While existing scientific search engines excel at delivering search results based on relational databases, they often neglect the analy
Externí odkaz:
http://arxiv.org/abs/2403.02576
In this report, we summarize the first NTIRE challenge on light field (LF) image super-resolution (SR), which aims at super-resolving LF images under the standard bicubic degradation with a magnification factor of 4. This challenge develops a new LF
Externí odkaz:
http://arxiv.org/abs/2304.10415
Exploiting spatial-angular correlation is crucial to light field (LF) image super-resolution (SR), but is highly challenging due to its non-local property caused by the disparities among LF images. Although many deep neural networks (DNNs) have been
Externí odkaz:
http://arxiv.org/abs/2302.08058
Autor:
Wu, Tianhao, Li, Boyang, Luo, Yihang, Wang, Yingqian, Xiao, Chao, Liu, Ting, Yang, Jungang, An, Wei, Guo, Yulan
Space-based infrared tiny ship detection aims at separating tiny ships from the images captured by earth orbiting satellites. Due to the extremely large image coverage area (e.g., thousands square kilometers), candidate targets in these images are mu
Externí odkaz:
http://arxiv.org/abs/2209.13756
Recent years have witnessed the great advances of deep neural networks (DNNs) in light field (LF) image super-resolution (SR). However, existing DNN-based LF image SR methods are developed on a single fixed degradation (e.g., bicubic downsampling), a
Externí odkaz:
http://arxiv.org/abs/2206.06214
Matching cost construction is a key step in light field (LF) depth estimation, but was rarely studied in the deep learning era. Recent deep learning-based LF depth estimation methods construct matching cost by sequentially shifting each sub-aperture
Externí odkaz:
http://arxiv.org/abs/2203.01576
Autor:
Wang, Yingqian, Wang, Longguang, Wu, Gaochang, Yang, Jungang, An, Wei, Yu, Jingyi, Guo, Yulan
Light field (LF) cameras record both intensity and directions of light rays, and encode 3D scenes into 4D LF images. Recently, many convolutional neural networks (CNNs) have been proposed for various LF image processing tasks. However, it is challeng
Externí odkaz:
http://arxiv.org/abs/2202.10603
Publikováno v:
In Wear 15 November 2024 556-557
Light field (LF) images can be used to improve the performance of image super-resolution (SR) because both angular and spatial information is available. It is challenging to incorporate distinctive information from different views for LF image SR. Mo
Externí odkaz:
http://arxiv.org/abs/2110.12114