Zobrazeno 1 - 10
of 68
pro vyhledávání: '"Zhao, Xile"'
Recently, implicit neural representations (INRs) have attracted increasing attention for multi-dimensional data recovery. However, INRs simply map coordinates via a multi-layer perception (MLP) to corresponding values, ignoring the inherent semantic
Externí odkaz:
http://arxiv.org/abs/2411.11356
The fusion of a low-resolution hyperspectral image (LR-HSI) with a high-resolution multispectral image (HR-MSI) has emerged as an effective technique for achieving HSI super-resolution (SR). Previous studies have mainly concentrated on estimating the
Externí odkaz:
http://arxiv.org/abs/2405.17818
Recently, we have witnessed the success of total variation (TV) for many imaging applications. However, traditional TV is defined on the original pixel domain, which limits its potential. In this work, we suggest a new TV regularization defined on th
Externí odkaz:
http://arxiv.org/abs/2405.17241
Nonlocal self-similarity (NSS) is an important prior that has been successfully applied in multi-dimensional data processing tasks, e.g., image and video recovery. However, existing NSS-based methods are solely suitable for meshgrid data such as imag
Externí odkaz:
http://arxiv.org/abs/2401.00708
The stripe noise existing in remote sensing images badly degrades the visual quality and restricts the precision of data analysis. Therefore, many destriping models have been proposed in recent years. In contrast to these existing models, in this pap
Externí odkaz:
http://arxiv.org/abs/2308.08866
Recently, tensor singular value decomposition (t-SVD) has emerged as a promising tool for hyperspectral image (HSI) processing. In the t-SVD, there are two key building blocks: (i) the low-rank enhanced transform and (ii) the accompanying low-rank ch
Externí odkaz:
http://arxiv.org/abs/2304.11141
Since higher-order tensors are naturally suitable for representing multi-dimensional data in real-world, e.g., color images and videos, low-rank tensor representation has become one of the emerging areas in machine learning and computer vision. Howev
Externí odkaz:
http://arxiv.org/abs/2212.00262
Autor:
Chang, Yi, Guo, Yun, Ye, Yuntong, Yu, Changfeng, Zhu, Lin, Zhao, Xile, Yan, Luxin, Tian, Yonghong
Most of the existing learning-based deraining methods are supervisedly trained on synthetic rainy-clean pairs. The domain gap between the synthetic and real rain makes them less generalized to complex real rainy scenes. Moreover, the existing methods
Externí odkaz:
http://arxiv.org/abs/2211.00837
Non-blind deblurring methods achieve decent performance under the accurate blur kernel assumption. Since the kernel uncertainty (i.e. kernel error) is inevitable in practice, semi-blind deblurring is suggested to handle it by introducing the prior of
Externí odkaz:
http://arxiv.org/abs/2210.05361
The deep convolutional neural network has achieved significant progress for single image rain streak removal. However, most of the data-driven learning methods are full-supervised or semi-supervised, unexpectedly suffering from significant performanc
Externí odkaz:
http://arxiv.org/abs/2203.13699