research-article

Adaptively Learning Low-high Frequency Information Integration for Pan-sharpening

Authors:

Feng ZhaoAuthors Info & Claims

MM '22: Proceedings of the 30th ACM International Conference on Multimedia

Pages 3375 - 3384

https://doi.org/10.1145/3503161.3547924

Published: 10 October 2022 Publication History

Abstract

Pan-sharpening aims to generate high-spatial resolution multi-spectral (MS) image by fusing high-spatial resolution panchromatic (PAN) image and its corresponding low-spatial resolution MS image. Despite the remarkable progress, most existing pan-sharpening methods only work in the spatial domain and rarely explore the potential solutions in the frequency domain. In this paper, we propose a novel pan-sharpening framework by adaptively learning low-high frequency information integration in the spatial and frequency dual domains. It consists of three key designs: mask prediction sub-network, low-frequency learning sub-network and high-frequency learning sub-network. Specifically, the first is responsible for measuring the modality-aware frequency information difference of PAN and MS images and further predicting the low-high frequency boundary in the form of a two-dimensional mask. In view of the mask, the second adaptively picks out the corresponding low-frequency components of different modalities and then restores the expected low-frequency one by spatial and frequency dual domains information integration while the third combines the above refined low-frequency and the original high-frequency for the latent high-frequency reconstruction. In this way, the low-high frequency information is adaptively learned, thus leading to the pleasing results. Extensive experiments validate the effectiveness of the proposed network and demonstrate the favorable performance against other state-of-the-art methods. The source code will be released at https://github.com/manman1995/pansharpening.

Supplementary Material

MP4 File (MM22-fp0737.mp4)

Presentation video of Adaptively Learning Low-high Frequency Information Integration for Pan-sharpening

Download
177.55 MB

References

[1]

Paolo Addesso, Gemine Vivone, Rocco Restaino, and Jocelyn Chanussot. 2020. A Data-Driven Model-Based Regression Applied to Panchromatic Sharpening. IEEE Transactions on Image Processing 29 (2020), 7779--7794.

Digital Library

[2]

Bruno Aiazzi, Stefano Baronti, and Massimo Selva. 2007. Improving component substitution pansharpening through multivariate regression of MS Pan data. IEEE Transactions on Geoscience and Remote Sensing 45, 10 (2007), 3230--3239.

[3]

L. Alparone, L. Wald, J. Chanussot, C. Thomas, P. Gamba, and L. M. Bruce. 2007. Comparison of Pansharpening Algorithms: Outcome of the 2006 GRS-S Data Fusion Contest. IEEE Transactions on Geoscience and Remote Sensing 45, 10 (2007), 3012--3021.

[4]

Jiajun Cai and Bo Huang. 2021. Super-Resolution-Guided Progressive Pansharpening Based on a Deep Convolutional Neural Network. IEEE Transactions on Geoscience and Remote Sensing 59, 6 (2021), 5206--5220.

[5]

Xiangyong Cao, Yang Chen, Qian Zhao, Deyu Meng, Yao Wang, Dong Wang, and Zongben Xu. 2015. Low-Rank Matrix Factorization under General Mixture Noise Distributions. In 2015 IEEE International Conference on Computer Vision (ICCV). 1493--1501.

Digital Library

[6]

Xiangyong Cao, Lin Xu, Deyu Meng, Qian Zhao, and Zongben Xu. 2017. Integration of 3-dimensional discrete wavelet transform and Markov random field for hyperspectral image classification. Neurocomputing 226 (2017), 90--100.

Digital Library

[7]

Xiangyong Cao, Zongben Xu, and Deyu Meng. 2019. Spectral-Spatial Hyperspectral Image Classification via Robust Low-Rank Feature Extraction and Markov Random Field. Remote. Sens. 11, 13 (2019), 1565.

[8]

Xiangyong Cao, Jing Yao, Zongben Xu, and Deyu Meng. 2020. Hyperspectral Image Classification With Convolutional Neural Network and Active Learning. IEEE Transactions on Geoscience and Remote Sensing 58, 7 (2020), 4604--4616.

[9]

Xiangyong Cao, Feng Zhou, Lin Xu, Deyu Meng, Zongben Xu, and John Paisley. 2018. Hyperspectral Image Classification With Markov Random Fields and a Convolutional Neural Network. IEEE Transactions on Image Processing 27, 5 (2018), 2354--2367.

Digital Library

[10]

Wjoseph Carper, Thomasm Lillesand, and Ralphw Kiefer. 1990. The use of intensity-hue-saturation transformations for merging SPOT panchromatic and multispectral image data. Photogrammetric Engineering and remote sensing 56, 4 (1990), 459--467.

[11]

Chen Chen, Yeqing Li, Wei Liu, and Junzhou Huang. 2015. SIRF: Simultaneous Satellite Image Registration and Fusion in a Unified Framework. IEEE Transactions on Image Processing 24, 11 (2015), 4213--4224.

Digital Library

[12]

Chao Dong, Chen Change Loy, Kaiming He, and Xiaoou Tang. 2016. Image Super-Resolution Using Deep Convolutional Networks. IEEE Transactions on Pattern Analysis and Machine Intelligence 38, 2 (2016), 295--307.

Digital Library

[13]

M. Frigo and S. G. Johnson. 1998. FFTW: An adaptive software architecture for the FFT. Acoustics, Speech, and Signal Processing, 1988. ICASSP-88., 1988 International Conference on 3 (1998).

[14]

Xueyang Fu, Zihuang Lin, Yue Huang, and Xinghao Ding. 2019. A variational pan-sharpening with local gradient constraints. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 10265--10274.

[15]

Xueyang Fu, Wu Wang, Yue Huang, Xinghao Ding, and John Paisley. 2021. Deep Multiscale Detail Networks for Multiband Spectral Image Sharpening. IEEE Transactions on Neural Networks and Learning Systems 32, 5 (2021), 2090--2104.

[16]

Ying Fu, Zhiyuan Liang, and Shaodi You. 2021. Bidirectional 3D Quasi-Recurrent Neural Network for Hyperspectral Image Super-Resolution. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 14 (2021), 2674-- 2688.

[17]

Dario Fuoli, Luc Van Gool, and Radu Timofte. 2021. Fourier Space Losses for Efficient Perceptual Image Super-Resolution. arXiv:2106.00783 [eess.IV]

[18]

Morteza Ghahremani and Hassan Ghassemian. 2016. Nonlinear IHS: A promising method for pan-sharpening. IEEE Geoscience and Remote Sensing Letters 13, 11 (2016), 1606--1610.

[19]

Alan R Gillespie, Anne B Kahle, and Richard E Walker. 1987. Color enhancement of highly correlated images. II. Channel ratio and "chromaticity" transformation techniques. Remote Sensing of Environment 22, 3 (1987), 343--365.

[20]

A. R. Gillespie, A. B. Kahle, and R. E. Walker. 1987. Color enhancement of highly correlated images. II. Channel ratio and "chromaticity" transformation techniques - ScienceDirect. Remote Sensing of Environment 22, 3 (1987), 343--365.

[21]

Juan Mario Haut, Mercedes E. Paoletti, Javier Plaza, Jun Li, and Antonio Plaza. 2018. Active Learning With Convolutional Neural Networks for Hyperspectral Image Classification Using a New Bayesian Approach. IEEE Transactions on Geoscience and Remote Sensing 56, 11 (2018), 6440--6461.

[22]

R. Haydn, G.W. Dalke, J. Henkel, and J. E. Bare. 1982. Application of the IHS color transform to the processing of multisensor data and image enhancement. National Academy of Sciences of the United States of America 79, 13 (1982), 571--577.

[23]

Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016. Deep Residual Learning for Image Recognition. In IEEE Conference on Computer Vision and Pattern Recognition. 770--778.

[24]

Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016. Deep Residual Learning for Image Recognition. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 770--778.

[25]

A. F. Goetz J. R. H. Yuhas and J. M. Boardman. 1992. Discrimination among semiarid landscape endmembers using the spectral angle mapper (SAM) algorithm. Proc. Summaries Annu. JPL Airborne Geosci. Workshop (1992), 147--149.

[26]

Junjun Jiang, Jiayi Ma, Chen Chen, Zhongyuan Wang, Zhihua Cai, and Lizhe Wang. 2018. SuperPCA: A Superpixelwise PCA Approach for Unsupervised Feature Extraction of Hyperspectral Imagery. IEEE Transactions on Geoscience and Remote Sensing 56, 8 (2018), 4581--4593.

[27]

Junjun Jiang, Jiayi Ma, and Xianming Liu. 2020. Multilayer Spectral-Spatial Graphs for Label Noisy Robust Hyperspectral Image Classification. IEEE Transactions on Neural Networks and Learning Systems (2020), 1--14.

[28]

Junjun Jiang, Jiayi Ma, ZhengWang, Chen Chen, and Xianming Liu. 2019. Hyperspectral Image Classification in the Presence of Noisy Labels. IEEE Transactions on Geoscience and Remote Sensing 57, 2 (2019), 851--865.

[29]

Junjun Jiang, He Sun, Xianming Liu, and Jiayi Ma. 2020. Learning Spatial-Spectral Prior for Super-Resolution of Hyperspectral Imagery. IEEE Transactions on Computational Imaging 6 (2020), 1082--1096.

[30]

Kui Jiang, Zhongyuan Wang, Peng Yi, and Junjun Jiang. 2018. A Progressively Enhanced Network for Video Satellite Imagery Superresolution. IEEE Signal Processing Letters 25, 11 (2018), 1630--1634.

[31]

Kui Jiang, Zhongyuan Wang, Peng Yi, Junjun Jiang, Guangcheng Wang, Zhen Han, and Tao Lu. 2019. GAN-Based Multi-level Mapping Network for Satellite Imagery Super-Resolution. In 2019 IEEE International Conference on Multimedia and Expo (ICME). 526--531.

[32]

Kui Jiang, Zhongyuan Wang, Peng Yi, Junjun Jiang, Emily Xiao, and Yuan Yao. 2018. Deep Distillation Recursive Network for Remote Sensing Imagery Super- Resolution. Remote Sensing 10 (10 2018), 1700.

[33]

Kui Jiang, Zhongyuan Wang, Peng Yi, Guangcheng Wang, Tao Lu, and Junjun Jiang. 2019. Edge-Enhanced GAN for Remote Sensing Image Superresolution. IEEE Transactions on Geoscience and Remote Sensing 57, 8 (2019), 5799--5812.

[34]

Muhammad Murtaza Khan, Jocelyn Chanussot, Laurent Condat, and Annick Montanvert. 2008. Indusion: Fusion of multispectral and panchromatic images using the induction scaling technique. IEEE Geoscience and Remote Sensing Letters 5, 1 (2008), 98--102.

[35]

P Kwarteng and A Chavez. 1989. Extracting spectral contrast in Landsat Thematic Mapper image data using selective principal component analysis. Photogrammetric Engineering and remote sensing 55, 339--348 (1989), 1.

[36]

C.A. Laben and B.V. Brower. 2000. Process for Enhancing the Spatial Resolution of Multispectral Imagery Using Pan-Sharpening. US Patent 6011875A (2000).

[37]

Craig A Laben and Bernard V Brower. 2000. Process for enhancing the spatial resolution of multispectral imagery using pan-sharpening. US Patent 6,011,875.

[38]

W. Liao, H. Xin, F. V. Coillie, G. Thoonen, and W. Philips. 2017. Two-stage fusion of thermal hyperspectral and visible RGB image by PCA and guided filter. In Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing.

[39]

J. G. Liu. 2000. Smoothing filter-based intensity modulation: A spectral preserve image fusion technique for improving spatial details. International Journal of Remote Sensing 21, 18 (2000), 3461--3472.

[40]

Xiaochen Lu, Junping Zhang, Dezheng Yang, Longting Xu, and FengDe Jia. 2021. Cascaded Convolutional Neural Network-Based Hyperspectral Image Resolution Enhancement via an Auxiliary Panchromatic Image. IEEE Transactions on Image Processing 30 (2021), 6815--6828.

Digital Library

[41]

Jiayi Ma, Han Xu, Junjun Jiang, Xiaoguang Mei, and Xiao-Ping Zhang. 2020. DDcGAN: A Dual-Discriminator Conditional Generative Adversarial Network for Multi-Resolution Image Fusion. IEEE Transactions on Image Processing 29 (2020), 4980--4995.

Digital Library

[42]

Jiayi Ma, Wei Yu, Chen Chen, Pengwei Liang, Xiaojie Guo, and Junjun Jiang. 2020. Pan-GAN: An unsupervised pan-sharpening method for remote sensing image fusion. Information Fusion 62 (2020), 110--120.

[43]

SG Mallat. 1989. ATheory for Multiresolution Signal Decomposition: TheWavelet Representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 11, 7 (1989), 674--693.

Digital Library

[44]

Giuseppe Masi, Davide Cozzolino, Luisa Verdoliva, and Giuseppe Scarpa. 2016. Pansharpening by Convolutional Neural Networks. Remote Sensing 8, 7 (2016).

[45]

Jorge Nunez, Xavier Otazu, Octavi Fors, Albert Prades, Vicenc Pala, and Roman Arbiol. 1999. Multiresolution-based image fusion with additive wavelet decomposition. IEEE Transactions on Geoscience and Remote sensing 37, 3 (1999), 1204--1211.

[46]

Robert A Schowengerdt. 1980. Reconstruction of multispatial, multispectral image data using spatial frequency content. Photogrammetric Engineering and Remote Sensing 46, 10 (1980), 1325--1334.

[47]

Vijay P. Shah, Nicolas H. Younan, and Roger L. King. 2008. An Efficient Pan- Sharpening Method via a Combined Adaptive PCA Approach and Contourlets. IEEE Transactions on Geoscience and Remote Sensing 46, 5 (2008), 1323--1335.

[48]

Xin Tian, Yuerong Chen, Changcai Yang, Xun Gao, and Jiayi Ma. 2020. A Variational Pansharpening Method Based on Gradient Sparse Representation. IEEE Signal Processing Letters 27 (2020), 1180--1184.

[49]

Xin Tian, Yuerong Chen, Changcai Yang, and Jiayi Ma. 2021. Variational Pansharpening by Exploiting Cartoon-Texture Similarities. IEEE Transactions on Geoscience and Remote Sensing (2021), 1--16.

[50]

Gemine Vivone, Luciano Alparone, Jocelyn Chanussot, Mauro Dalla Mura, Andrea Garzelli, Giorgio A Licciardi, Rocco Restaino, and Lucien Wald. 2014. A critical comparison among pansharpening algorithms. IEEE Transactions on Geoscience and Remote Sensing 53, 5 (2014), 2565--2586.

[51]

Lucien Wald, Thierry Ranchin, and Marc Mangolini. 1997. Fusion of satellite images of different spatial resolutions: Assessing the quality of resulting images. Photogrammetric Engineering and Remote Sensing 63 (11 1997), 691--699.

[52]

Xinya Wang, Jiayi Ma, and Junjun Jiang. 2021. Hyperspectral Image Super- Resolution via Recurrent Feedback Embedding and Spatial-Spectral Consistency Regularization. IEEE Transactions on Geoscience and Remote Sensing (2021), 1--13.

[53]

XiaoWu, Ting-Zhu Huang, Liang-Jian Deng, and Tian-Jing Zhang. 2021. Dynamic Cross Feature Fusion for Remote Sensing Pansharpening. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV). 14687--14696.

[54]

Han Xu, Jiayi Ma, Zhenfeng Shao, Hao Zhang, Junjun Jiang, and Xiaojie Guo. 2021. SDPNet: A Deep Network for Pan-Sharpening With Enhanced Information Representation. IEEE Transactions on Geoscience and Remote Sensing 59, 5 (2021), 4120--4134.

[55]

Shuang Xu, Jiangshe Zhang, Zixiang Zhao, Kai Sun, Junmin Liu, and Chunxia Zhang. 2021. Deep Gradient Projection Networks for Pan-sharpening. In IEEE Conference on Computer Vision and Pattern Recognition. 1366--1375.

[56]

Keyu Yan, Man Zhou, Liu Liu, Chengjun Xie, and Danfeng Hong. 2022. When Pansharpening Meets Graph Convolution Network and Knowledge Distillation. IEEE Transactions on Geoscience and Remote Sensing 60 (2022), 1--15. https: //doi.org/10.1109/TGRS.2022.3168192

[57]

Gang Yang, Man Zhou, Keyu Yan, Aiping Liu, Xueyang Fu, and Fan Wang. 2022. Memory-Augmented Deep Conditional Unfolding Network for Pan-Sharpening. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 1788--1797.

[58]

Junfeng Yang, Xueyang Fu, Yuwen Hu, Yue Huang, Xinghao Ding, and John Paisley. 2017. PanNet: A deep network architecture for pan-sharpening. In IEEE International Conference on Computer Vision. 5449--5457.

[59]

Qiangqiang Yuan, Yancong Wei, Xiangchao Meng, Huanfeng Shen, and Liangpei Zhang. 2018. A multiscale and multidepth convolutional neural network for remote sensing imagery pan-sharpening. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 11, 3 (2018), 978--989.

[60]

Q. Yuan, Y. Wei, X. Meng, H. Shen, and L. Zhang. 2018. A Multiscale and Multidepth Convolutional Neural Network for Remote Sensing Imagery Pan- Sharpening. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 11, 3 (2018), 978--989.

[61]

Hao Zhang and Jiayi Ma. 2021. GTP-PNet: A residual learning network based on gradient transformation prior for pansharpening. ISPRS Journal of Photogrammetry and Remote Sensing 172 (2021), 223--239.

[62]

Yulun Zhang, Kunpeng Li, Kai Li, LichenWang, Bineng Zhong, and Yun Fu. 2018. Image super-resolution using very deep residual channel attention networks. In European Conference on Computer Vision. 286--301.

[63]

Man Zhou, Xueyang Fu, Jie Huang, Feng Zhao, Aiping Liu, and Rujing Wang. 2022. Effective Pan-Sharpening With Transformer and Invertible Neural Network. IEEE Transactions on Geoscience and Remote Sensing 60 (2022), 1--15. https: //doi.org/10.1109/TGRS.2021.3137967

[64]

Man Zhou, Jie Huang, Yanchi Fang, Xueyang Fu, and Aiping Liu. 2022. Pan- Sharpening with Customized Transformer and Invertible Neural Network. AAAI Press.

[65]

Man Zhou, Zeyu Xiao, Xueyang Fu, Aiping Liu, Gang Yang, and Zhiwei Xiong. 2021. Unfolding Taylor's Approximations for Image Restoration. In NeurIPS.

[66]

Man Zhou, Keyu Yan, Jie Huang, Zihe Yang, Xueyang Fu, and Feng Zhao. 2022. Mutual Information-Driven Pan-Sharpening. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 1798--1808.

[67]

Man Zhou, Keyu Yan, Jinshan Pan, Wenqi Ren, Qiaokang Xie, and Xiangyong Cao. 2022. Memory-augmented Deep Unfolding Network for Guided Image Super-resolution. ArXiv abs/2203.04960 (2022).

[68]

Tian-Jing Zhang Xiaoxu Jin Zi-Rong Jin, Liang-Jian Deng. 2021. BAM: Bilateral Activation Mechanism for Image Fusion. Proceedings of the 29th ACMInternational Conference on Multimedia (ACM MM) (2021).

Digital Library

Cited By

Liu ZGong JLu HPan XLan R(2025)FFENet: Learning Frequency Features for Low-Light EnhancementIEEE Transactions on Instrumentation and Measurement10.1109/TIM.2024.349714074(1-13)Online publication date: 2025
https://doi.org/10.1109/TIM.2024.3497140
Zou ZYu HHuang JZhao FCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)FreqMamba: Viewing Mamba from a Frequency Perspective for Image DerainingProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3680862(1905-1914)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3664647.3680862
Wang XZhao LZhang JWang ABai H(2024)A Wavelet-Domain Consistency-Constrained Compressive Sensing Framework Based on Memory-Boosted Guidance FilteringIEEE Transactions on Instrumentation and Measurement10.1109/TIM.2024.339809673(1-16)Online publication date: 2024
https://doi.org/10.1109/TIM.2024.3398096
Show More Cited By

Index Terms

Adaptively Learning Low-high Frequency Information Integration for Pan-sharpening
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Image and video acquisition
        Hyperspectral imaging

Recommendations

Learning High-frequency Feature Enhancement and Alignment for Pan-sharpening
MM '23: Proceedings of the 31st ACM International Conference on Multimedia

Pan-sharpening aims to utilize the high-resolution panchromatic (PAN) image as a guidance to super-resolve the spatial resolution of the low-resolution multispectral (MS) image. The key challenge in pan-sharpening is how to effectively and precisely ...
Pan-sharpening via regional division and NSST

In this paper, a novel Pan-sharpening algorithm for high resolution Panchromatic (HR PAN) and low resolution multispectral image (LR MS) via regional division and Non-sampled shift-invariance shearlet transform (NSST) is proposed. The purpose of our ...
Framelet based pan-sharpening via a variational method

Pan-sharpening is a process of combining a low resolution multi-spectral (MS) image and a high resolution panchromatic (PAN) image to obtain a single high resolution MS image. In this paper, we propose two pan-sharpening methods based on the framelet ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

MM '22: Proceedings of the 30th ACM International Conference on Multimedia

October 2022

7537 pages

ISBN:9781450392037

DOI:10.1145/3503161

General Chairs:
João Magalhães
NOVA University of Lisbon, Portugal
,
Alberto del Bimbo
University of Florence, Italy
,
Shin'ichi Satoh
National Institute of Informatics, Japan
,
Nicu Sebe
University of Trento, Italy
,
Program Chairs:
Xavier Alameda-Pineda
Inria, Grenoble, France
,
Qin Jin
Renmin University of China, China
,
Vincent Oria
New Jersey Institute of Technology, USA
,
Laura Toni
University College London, UK

Copyright © 2022 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

SIGMM: ACM Special Interest Group on Multimedia

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 10 October 2022

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Funding Sources

MindSpore, CANN, and Ascend AI Processor

Conference

MM '22

Sponsor:

SIGMM

MM '22: The 30th ACM International Conference on Multimedia

October 10 - 14, 2022

Lisboa, Portugal

Acceptance Rates

Overall Acceptance Rate 2,145 of 8,556 submissions, 25%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

27
Total Citations
View Citations
640
Total Downloads

Downloads (Last 12 months)172
Downloads (Last 6 weeks)10

Reflects downloads up to 03 Mar 2025

Other Metrics

View Author Metrics

Citations

Cited By

Liu ZGong JLu HPan XLan R(2025)FFENet: Learning Frequency Features for Low-Light EnhancementIEEE Transactions on Instrumentation and Measurement10.1109/TIM.2024.349714074(1-13)Online publication date: 2025
https://doi.org/10.1109/TIM.2024.3497140
Zou ZYu HHuang JZhao FCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)FreqMamba: Viewing Mamba from a Frequency Perspective for Image DerainingProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3680862(1905-1914)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3664647.3680862
Wang XZhao LZhang JWang ABai H(2024)A Wavelet-Domain Consistency-Constrained Compressive Sensing Framework Based on Memory-Boosted Guidance FilteringIEEE Transactions on Instrumentation and Measurement10.1109/TIM.2024.339809673(1-16)Online publication date: 2024
https://doi.org/10.1109/TIM.2024.3398096
Li YJin WQiu SHe Y(2024)Multiscale Spatial-Frequency Domain Dynamic Pansharpening of Remote Sensing Images Integrated With Wavelet TransformIEEE Transactions on Geoscience and Remote Sensing10.1109/TGRS.2024.346366962(1-15)Online publication date: 2024
https://doi.org/10.1109/TGRS.2024.3463669
Shen HDing HZhang YCong XZhao ZJiang X(2024)Spatial-Frequency Adaptive Remote Sensing Image Dehazing With Mixture of ExpertsIEEE Transactions on Geoscience and Remote Sensing10.1109/TGRS.2024.345898662(1-14)Online publication date: 2024
https://doi.org/10.1109/TGRS.2024.3458986
Xing YQu LZhang SFeng JZhang XZhang Y(2024)Empower Generalizability for Pansharpening Through Text-Modulated Diffusion ModelIEEE Transactions on Geoscience and Remote Sensing10.1109/TGRS.2024.343443162(1-12)Online publication date: 2024
https://doi.org/10.1109/TGRS.2024.3434431
Zhu HYi XLi XHou BJiao CMa WJiao L(2024)ConvGRU-Based Multiscale Frequency Fusion Network for PAN-MS Joint ClassificationIEEE Transactions on Geoscience and Remote Sensing10.1109/TGRS.2024.341537162(1-15)Online publication date: 2024
https://doi.org/10.1109/TGRS.2024.3415371
Chi KLi JJing WLi QWang Q(2024)Neural Implicit Fourier Transform for Remote Sensing Shadow RemovalIEEE Transactions on Geoscience and Remote Sensing10.1109/TGRS.2024.341236862(1-10)Online publication date: 2024
https://doi.org/10.1109/TGRS.2024.3412368
Chen LPan JYang JWang M(2024)CS-Net: Deep Multibranch Network Considering Scene Features for Sharpness Assessment of Remote Sensing ImagesIEEE Transactions on Geoscience and Remote Sensing10.1109/TGRS.2024.337762962(1-14)Online publication date: 2024
https://doi.org/10.1109/TGRS.2024.3377629
Zhang JHe XYan KCao KLi RXie CZhou MHong D(2024)Pan-Sharpening With Wavelet-Enhanced High-Frequency InformationIEEE Transactions on Geoscience and Remote Sensing10.1109/TGRS.2024.336716562(1-14)Online publication date: 2024
https://doi.org/10.1109/TGRS.2024.3367165
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Figures

Tables

Media

View Table of Conten