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Jiang-She Zhang

7 papers hereh-index 9760 citations26 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author2
  • middle author4

Across the 6 of 7 papers where every author was matched, so the position is known.

fields
  • cs.CV4
  • stat.ML2
  • physics.geo-ph1
same name
  • Jiang-She Zhang — 30 papers, h 8
  • Jiang-She Zhang — 4 papers, h 4

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

most citedDDFM: Denoising Diffusion Model for Multi-Modality Image Fusion

11 citations · 27 across the 7 of their papers we have counts for

collaborators
Showing cs.CVShow all

4 papers · 1 filter

cs.CV2023★ 1 cited

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…

cs.CV2023★ 1 cited

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…

cs.CV2023★ 4 cited

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…

cs.CV2023★ 11 cited

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…

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