11 citations · 27 across the 7 of their papers we have counts for
7 papers
Seismic Data Interpolation via Denoising Diffusion Implicit Models with Coherence-corrected Resampling
Xiaoli Wei, Chunxia Zhang, Hongtao Wang +5
Accurate interpolation of seismic data is crucial for improving the quality of imaging and interpretation. In recent years, deep learning models such as U-Net and generative advers…
UPNet: Uncertainty-based Picking Deep Learning Network for Robust First Break Picking
Hongtao Wang, Jiangshe Zhang, Xiaoli Wei +2
In seismic exploration, first break (FB) picking is a crucial aspect in the determination of subsurface velocity models, significantly influencing the placement of wells. Many deep…
Equivariant Multi-Modality Image Fusion
Zixiang Zhao, Haowen Bai, Jiangshe Zhang +6
Multi-modality image fusion is a technique that combines information from different sensors or modalities, enabling the fused image to retain complementary features from each modal…
Spherical Space Feature Decomposition for Guided Depth Map Super-Resolution
Zixiang Zhao, Jiangshe Zhang, Xiang Gu +5
Guided depth map super-resolution (GDSR), as a hot topic in multi-modal image processing, aims to upsample low-resolution (LR) depth maps with additional information involved in hi…
DDFM: Denoising Diffusion Model for Multi-Modality Image Fusion
Zixiang Zhao, Haowen Bai, Yuanzhi Zhu +7
Multi-modality image fusion aims to combine different modalities to produce fused images that retain the complementary features of each modality, such as functional highlights and…
An information-theoretic learning model based on importance sampling
Jiangshe Zhang, Lizhen Ji, Fei Gao +1
A crucial assumption underlying the most current theory of machine learning is that the training distribution is identical to the test distribution. However, this assumption may no…
Information Theoretical Importance Sampling Clustering
Jiangshe Zhang, Lizhen Ji, Meng Wang
A current assumption of most clustering methods is that the training data and future data are taken from the same distribution. However, this assumption may not hold in most real-w…