多模态信息融合

A Two-level Radial-velocity Zero-point Calibration for LAMOST MRS with Gaia and APOGEE and a Value-added RV Catalogue

基于Gaia和APOGEE的LAMOST MRS的两级视向速度零点校准及增值视向速度星表

Jinming Zhang, Haibo Yuan

专题命中 多聚焦/多曝光融合 ：multi-exposure(abstract)

AI总结 该研究针对LAMOST MRS的RV零点系统误差，提出两级校准方法并发布增值星表，使RV精度提升约2倍，支持跨多维度的一致视向速度分析。

Comments 18 pages, 12 figures. Catalogue available at https://doi.org/10.5281/zenodo.21467895 ; pipeline at https://github.com/CrotRest/lamost-mrs-rvzp. Accepted by ApJS

Multivariable Mendelian randomization with weak instruments: a comparison of Bayesian and frequentist methods

弱工具变量下的多变量孟德尔随机化：贝叶斯与频率学方法的比较

Andrew J. Grant, Ashish Patel, Stephen Burgess

专题命中 多聚焦/多曝光融合 ：multi-exposure(abstract)

AI总结 本文比较了贝叶斯方法MVMR-Pony与频率学方法在多变量孟德尔随机化中处理弱工具变量偏差的性能，模拟显示MVMR-Pony在偏差、覆盖率和统计功效方面更优。

Comments 29 pages, 10 Figures

DPA4: Pushing the Accuracy-Cost Frontier of Interatomic Potentials with EMFA SO(2) Convolution

DPA4: 利用EMFA SO(2)卷积推动原子间势的精度-成本前沿

Tiancheng Li, Wentao Li, Anyang Peng, Jianming Xue, Linfeng Zhang, Duo Zhang, Han Wang

专题命中 多聚焦/多曝光融合 ：multi-focus(abstract)

AI总结 本文提出DPA4架构，通过EMFA SO(2)等变卷积和编译器友好的训练路径，在降低参数和训练成本的同时，在Matbench Discovery等基准上达到最优精度-成本平衡。

Experimental and Numerical Study of Acoustic Streaming in Mid-Air Phased Arrays

中空相位阵列中声流的实验与数值研究

Christopher Stone, Yusuke Koroyasu, Yoichi Ochiai, Akiko Kaneko, Bruce W. Drinkwater, Tatsuki Fushimi

专题命中 多聚焦/多曝光融合 ：multi-focus(abstract)

AI总结 研究中空相位阵列中声流的动力学，通过实验和数值模拟揭示声流速度及侧瓣流动特性，为超声技术优化提供指导。

Multi focus acoustic field generation using Dammann gratings for phased array transducers

Tatsuki Fushimi, Yusuke Koroyasu

专题命中 多聚焦/多曝光融合 ：multi-focus(abstract)

Comments Uploading the published version (CC-BY Open Access License)

Journal ref Results in Physics 2024, 108040

Uncertainty of line-of-sight velocity measurement of faint stars from low and medium resolution optical spectra

László Dobos, Alexander S. Szalay, Tamás Budavári, Evan N. Kirby, Robert H. Lupton, Rosemary F. G. Wyse

专题命中 多聚焦/多曝光融合 ：multi-exposure(abstract)

Comments 31 pages, 10 figures (accepted to Apj)

Semiparametric Discovery and Estimation of Interaction in Mixed Exposures using Stochastic Interventions

David B. McCoy, Alan E. Hubbard, Alejandro Schuler, Mark J. van der Laan

专题命中 多聚焦/多曝光融合 ：multi-exposure(abstract)

Super-resolution imaging using super-oscillatory diffractive neural networks

Hang Chen, Sheng Gao, Zejia Zhao, Zhengyang Duan, Haiou Zhang, Gordon Wetzstein, Xing Lin

专题命中 多聚焦/多曝光融合 ：multi-focus(abstract)

Comments 18 pages, 7 figures, 1 table

Improved Three-Dimensional Reconstructions in Electron Ptychography through Defocus Series Measurements

Marcel Schloz, Thomas C. Pekin, Hamish G. Brown, Dana O. Byrne, Bryan D. Esser, Emmanuel Terzoudis-Lumsden, Takashi Taniguchi, Kenji Watanabe, Scott D. Findlay, Benedikt Haas, Jim Ciston, Christoph T. Koch

专题命中 多聚焦/多曝光融合 ：multi-focus(abstract)

Focus-style proofs for the two-way alternation-free $μ$-calculus

Jan Rooduijn, Yde Venema

专题命中 多聚焦/多曝光融合 ：multi-focus(abstract)

Comments To appear in proceedings of WoLLIC 2023

Coherent Stokes Raman scattering microscopy (CSRS)

Sandro Heuke, Hervé Rigneault

专题命中 多聚焦/多曝光融合 ：multi-focus(abstract)

Analyzing Behavioral Changes of Twitter Users After Exposure to Misinformation

Yichen Wang, Richard Han, Tamara Lehman, Qin Lv, Shivakant Mishra

专题命中 多聚焦/多曝光融合 ：multi-exposure(abstract)

Comments Accepted to FOSINT-SI, co-located with ASONAM 2021

Ultrafast photonic rainbow with controllable orbital angular momentum

Shunlin Huang, Peng Wang, Xiong Shen, Jun Liu, Ruxin Li

专题命中 多聚焦/多曝光融合 ：multi-focus(abstract)

Comments 11 pages, 7 figures

Fibonacci terahertz imaging by silicon diffractive optics

Domas Jokubauskis, Linas Minkevičius, Mindaugas Karaliūnas, Simonas Indrišiūnas, Irmantas Kašalynas, Gediminas Račiukaitis, Gintaras Valušis

专题命中 多聚焦/多曝光融合 ：multi-focus(abstract)

Comments 4 pages, 6 figures

Journal ref Opt. Lett. 43, 2795-2798 (2018)

A New Population of Planetary Nebulae Discovered in the Large Magellanic Cloud (I): Preliminary Sample

Warren A. Reid, Quentin A. Parker

专题命中 多聚焦/多曝光融合 ：multi-exposure(abstract)

Comments 14 pages, 21 figures (zipped). Accepted for publication in Mmonthly Notices of the Royal Astronomical Society

Journal ref AIP Conf.Proc.804:12-12,2006; Mon.Not.Roy.Astron.Soc.365:401-413,2006

H-alpha Stacked Images Reveal Large Numbers of PNe in the LMC

Warren A. Reid, Quentin A. Parker

专题命中 多聚焦/多曝光融合 ：multi-exposure(abstract)

Comments 6 pages, 3 figures, LaTeX, to be published in Proceedings of the ESO workshop on Planetary Nebulae beyond the Milky Way held at ESO, Garching, May 19-21, 2004

Dual Modality Prompted Diffusion Priors for Zero Shot Hyperspectral Pansharpening

用于零样本高光谱 pansharpening 的双模态提示扩散先验

Pengwei Xie, Fei Zhu, Jiajun Li, Xiangyuan Liu, Xiangyuan Liu, Kangqing Shen, Gemine Vivone

机构 * School of Artificial Intelligence, Beijing Normal University（北京师范大学人工智能学院） ； Peking University（北京大学） ； National Center for Applied Mathematics Shenzhen (NCAMS), Southern University of Science and Technology（南方科技大学深圳应用数学国家中心） ； Department of Automation, Tsinghua University（清华大学自动化系） ； National Research Council of Italy, Institute of Integrated Methodologies for Earth Observation (CNR-IMIOT)（意大利国家研究委员会综合地球观测方法研究所） 纠错

专题命中 遥感融合与全色锐化 ：pansharpening(title,title_cn)；分类 cs.CV

AI总结 该研究针对零样本高光谱 pansharpening 问题，提出双模态提示扩散模型 DIDM，通过双模态提示注入与全色引导正则化实现空间细节与光谱保真的平衡，在多数据集实验中取得最优性能。

Multi-Head Attention Residual Unfolded Network for Model-Based Pansharpening

Ivan Pereira-Sánchez, Eloi Sans, Julia Navarro, Joan Duran

专题命中 遥感融合与全色锐化 ：pansharpening(title,abstract)；image fusion(abstract)；分类 cs.CV、eess.IV

Linearly-evolved Transformer for Pan-sharpening

Junming Hou, Zihan Cao, Naishan Zheng, Xuan Li, Xiaoyu Chen, Xinyang Liu, Xiaofeng Cong, Man Zhou, Danfeng Hong

专题命中 遥感融合与全色锐化 ：pan-sharpening(title,abstract)；image fusion(abstract)；分类 cs.CV、eess.IV

Comments 10 pages

SSDiff: Spatial-spectral Integrated Diffusion Model for Remote Sensing Pansharpening

Yu Zhong, Xiao Wu, Liang-Jian Deng, Zihan Cao

专题命中 遥感融合与全色锐化 ：pansharpening(title,abstract)；image fusion(abstract)；分类 cs.CV、eess.IV

PC-GANs: Progressive Compensation Generative Adversarial Networks for Pan-sharpening

Yinghui Xing, Shuyuan Yang, Song Wang, Yan Zhang, Yanning Zhang

专题命中 遥感融合与全色锐化 ：pan-sharpening(title,abstract)；pansharpening(abstract)；分类 cs.CV、eess.IV

Comments 36 pages

Efficient and Accurate Hyperspectral Pansharpening Using 3D VolumeNet and 2.5D Texture Transfer

Yinao Li, Yutaro Iwamoto, Ryousuke Nakamura, Lanfen Lin, Ruofeng Tong, Yen-Wei Chen

专题命中 遥感融合与全色锐化 ：pansharpening(title,abstract)；image fusion(abstract)；分类 cs.CV、eess.IV

Model-based image adjustment for a successful pansharpening

Gintautas Palubinskas

专题命中 遥感融合与全色锐化 ：pansharpening(title,abstract)；image fusion(abstract)；分类 cs.CV、eess.IV

Comments 17 pages, 6 figures, 7 tables

Recent Advances and New Guidelines on Hyperspectral and Multispectral Image Fusion

Renwei Dian, Shutao Li, Bin Sun, Anjing Guo

专题命中 遥感融合与全色锐化 ：image fusion(title,abstract)；pan-sharpening(abstract)；分类 cs.CV、eess.IV

Universal Pansharpening Model

通用全分辨率融合基础模型

Hebaixu Wang, Jing Zhang, Haonan Guo, Di Wang, Jiayi Ma, Bo Du, Liangpei Zhang

机构 * School of Electronic Information, Wuhan University（武汉大学电子信息学院） ； School of Computer Science, Wuhan University（武汉大学计算机学院） ； State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University（武汉大学测绘遥感信息工程国家重点实验室） ； Zhongguancun Academy（中关村学院） 纠错

专题命中 遥感融合与全色锐化 ：pansharpening(title,abstract)；information fusion(abstract)；分类 cs.CV

AI总结 FoundPS是一种通用全分辨率融合基础模型，通过模态交错Transformer和潜在扩散桥梁模型实现跨传感器和场景的稳健融合，提升全分辨率融合的泛化能力和鲁棒性。

Fose: Fusion of One-Step Diffusion and End-to-End Network for Pansharpening

Fose：一阶段扩散与端到端网络融合用于全色增强

Kai Liu, Zeli Lin, Weibo Wang, Linghe Kong, Yulun Zhang

机构 * Shanghai Jiao Tong University（上海交通大学） 纠错

专题命中 遥感融合与全色锐化 ：pansharpening(title,abstract)；image fusion(abstract)；分类 cs.CV

AI总结 Fose通过融合一步扩散模型与端到端网络，实现全色增强的高效高精度处理，提升速度7.42倍并优于基线模型。

Comments Code link: https://github.com/Kai-Liu001/Fose

Leveraging Large-Scale Pretrained Spatial-Spectral Priors for General Zero-Shot Pansharpening

利用大规模预训练的空间-光谱先验进行通用零样本全色融合

Yongchuan Cui, Peng Liu, Yi Zeng

专题命中 遥感融合与全色锐化 ：pansharpening(title,abstract)；image fusion(abstract)；分类 cs.CV

AI总结 本文提出利用大规模模拟数据预训练空间-光谱先验，提升遥感图像融合在不同传感器和条件下的泛化能力。

Pansharpening of PRISMA products for archaeological prospection

Gregory Sech, Giulio Poggi, Marina Ljubenovic, Marco Fiorucci, Arianna Traviglia

专题命中 遥感融合与全色锐化 ：pansharpening(title,abstract)；pan-sharpening(abstract)；分类 cs.CV

Journal ref IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium

PanAdapter: Two-Stage Fine-Tuning with Spatial-Spectral Priors Injecting for Pansharpening

RuoCheng Wu, ZiEn Zhang, ShangQi Deng, YuLe Duan, LiangJian Deng

专题命中 遥感融合与全色锐化 ：pansharpening(title,abstract)；image fusion(abstract)；分类 cs.CV

Probability-based Global Cross-modal Upsampling for Pansharpening

Zeyu Zhu, Xiangyong Cao, Man Zhou, Junhao Huang, Deyu Meng

专题命中 遥感融合与全色锐化 ：pansharpening(title,abstract)；pan-sharpening(abstract)；分类 cs.CV

Comments 10 pages, 5 figures