9.2k citations · 12.1k across the 87 of their papers we have counts for
11 papers · 1 filter
S2S-WTV: Seismic Data Noise Attenuation Using Weighted Total Variation Regularized Self-Supervised Learning
Zitai Xu, Yisi Luo, Bangyu Wu +1
Seismic data often undergoes severe noise due to environmental factors, which seriously affects subsequent applications. Traditional hand-crafted denoisers such as filters and regu…
Fast Noise Removal in Hyperspectral Images via Representative Coefficient Total Variation
Jiangjun Peng, Hailin Wang, Xiangyong Cao +3
Mining structural priors in data is a widely recognized technique for hyperspectral image (HSI) denoising tasks, whose typical ways include model-based methods and data-based metho…
A Learnable Optimization and Regularization Approach to Massive MIMO CSI Feedback
Zhengyang Hu, Guanzhang Liu, Qi Xie +3
Channel state information (CSI) plays a critical role in achieving the potential benefits of massive multiple input multiple output (MIMO) systems. In frequency division duplex (FD…
KXNet: A Model-Driven Deep Neural Network for Blind Super-Resolution
Jiahong Fu, Hong Wang, Qi Xie +3
Although current deep learning-based methods have gained promising performance in the blind single image super-resolution (SISR) task, most of them mainly focus on heuristically co…
Decoupled-and-Coupled Networks: Self-Supervised Hyperspectral Image Super-Resolution with Subpixel Fusion
Danfeng Hong, Jing Yao, Deyu Meng +2
Enormous efforts have been recently made to super-resolve hyperspectral (HS) images with the aid of high spatial resolution multispectral (MS) images. Most prior works usually perf…
Two-Stream Graph Convolutional Network for Intra-oral Scanner Image Segmentation
Yue Zhao, Lingming Zhang, Yang Liu +6
Precise segmentation of teeth from intra-oral scanner images is an essential task in computer-aided orthodontic surgical planning. The state-of-the-art deep learning-based methods…
Diagnosing Batch Normalization in Class Incremental Learning
Minghao Zhou, Quanziang Wang, Jun Shu +2
Extensive researches have applied deep neural networks (DNNs) in class incremental learning (Class-IL). As building blocks of DNNs, batch normalization (BN) standardizes intermedia…
Low-light Image Enhancement by Retinex Based Algorithm Unrolling and Adjustment
Xinyi Liu, Qi Xie, Qian Zhao +2
Motivated by their recent advances, deep learning techniques have been widely applied to low-light image enhancement (LIE) problem. Among which, Retinex theory based ones, mostly f…
CMW-Net: Learning a Class-Aware Sample Weighting Mapping for Robust Deep Learning
Jun Shu, Xiang Yuan, Deyu Meng +1
Modern deep neural networks can easily overfit to biased training data containing corrupted labels or class imbalance. Sample re-weighting methods are popularly used to alleviate t…
Exact Decomposition of Joint Low Rankness and Local Smoothness Plus Sparse Matrices
Jiangjun Peng, Yao Wang, Hongying Zhang +2
It is known that the decomposition in low-rank and sparse matrices (\textbf{L+S} for short) can be achieved by several Robust PCA techniques. Besides the low rankness, the local sm…
Variational Inverting Network for Statistical Inverse Problems of Partial Differential Equations
Junxiong Jia, Yanni Wu, Peijun Li +1
To quantify uncertainties in inverse problems of partial differential equations (PDEs), we formulate them into statistical inference problems using Bayes' formula. Recently, well-j…