Pansharpening

Description from the archive archive 2025-07-28.

Benchmarks archive 2025-07-28

10 leaderboard tables shown for this task, 10 with rows (a “benchmark” on this site is a table with at least one row, as on /sota), ordered by row count. “Best model” is the first row in the archive's own order at snapshot; nothing is re-ranked here and metric direction is not recorded in the archive. PwC's Trend sparklines are not in the archive, so that column is omitted.

Syntology column: samples harvested from the paper's repositories and executed on synthesized fixtures; “ran” is not a correctness claim and does not order the table. A dash means no Syntology record for that paper, not a recorded non-run. Read from the graph 2026-09-24.

Libraries

Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.

Datasets archive 2025-07-28

4 datasets whose archive record lists this task, ordered by the archive's paper count.

Subtasks archive 2025-07-28

No subtask under this task in the archive's task tree.

Parent tasks archive 2025-07-28

Most implemented papers archive 2025-07-28

30 shown of 36 papers with code (78 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• SSDiff: Spatial-spectral Integrated Diffusion Model for Remote Sensing Pansharpening
  17 Apr 2024 3 repositories listed
  Pansharpening is a significant image fusion technique that merges the spatial content and spectral characteristics of remote sensing images to generate high-resolution multispectral images.
• Variational Zero-shot Multispectral Pansharpening
  9 Jul 2024 2 repositories listed
  The most challenging issue for this task is that only the to-be-fused LRMS and PAN are available, and the existing deep learning-based methods are unsuitable since they rely on many training pairs.
• Unsupervised Deep Learning-based Pansharpening with Jointly-Enhanced Spectral and Spatial Fidelity
  26 Jul 2023 2 repositories listed
  In latest years, deep learning has gained a leading role in the pansharpening of multiresolution images.
• Pansharpening by convolutional neural networks in the full resolution framework
  16 Nov 2021 2 repositories listed
  A further problem is the scarcity of training data, which causes a limited generalization ability and a poor performance on off-training test images.
• DiffFuSR: Super-Resolution of all Sentinel-2 Multispectral Bands using Diffusion Models
  13 Jun 2025 1 repository listed
  This paper presents DiffFuSR, a modular pipeline for super-resolving all 12 spectral bands of Sentinel-2 Level-2A imagery to a unified ground sampling distance (GSD) of 2.
• Zero-Shot Hyperspectral Pansharpening Using Hysteresis-Based Tuning for Spectral Quality Control
  22 May 2025 1 repository listed
  During fine-tuning, the spatial loss is turned on and off to ensure a fast convergence of the spectral loss to the desired level, according to a hysteresis-like dynamic.
• A General Adaptive Dual-level Weighting Mechanism for Remote Sensing Pansharpening
  17 Mar 2025 1 repository listed Syntology ran 1 of 1 samples · 0 unverified
  We use the covariance matrix to model the feature heterogeneity and redundancy and propose Correlation-Aware Covariance Weighting (CACW) to adjust them.
• Adaptive Rectangular Convolution for Remote Sensing Pansharpening
  1 Mar 2025 1 repository listed Syntology ran 1 of 1 samples · 0 unverified · 1 pointer-only (licence)
  ARConv adaptively learns both the height and width of the convolutional kernel and dynamically adjusts the number of sampling points based on the learned scale.
• Wavelet-Assisted Multi-Frequency Attention Network for Pansharpening
  7 Feb 2025 1 repository listed
  Then, we generate Frequency-Query, Spatial-Key, and Fusion-Value based on the physical meanings represented by different features, which enables a more effective capture of specific information in the frequency domain.
• Hyperspectral Pansharpening via Diffusion Models with Iteratively Zero-Shot Guidance
  1 Jan 2025 1 repository listed
  In this work, we propose a novel guided diffusion scheme with zero-shot guidance and neural spatial-spectral decomposition (NSSD) to iteratively generate the RGB detail image and map the RGB detail image to target…
• Dual-Granularity Semantic Guided Sparse Routing Diffusion Model for General Pansharpening
  1 Jan 2025 1 repository listed
  To address the domain gap produced by varying satellite sensors and distinct scenes, we propose a dual-granularity semantic guided sparse routing diffusion model for general pansharpening.
• Multi-Head Attention Residual Unfolded Network for Model-Based Pansharpening
  4 Sep 2024 1 repository listed
  The objective of pansharpening and hypersharpening is to accurately combine a high-resolution panchromatic (PAN) image with a low-resolution multispectral (MS) or hyperspectral (HS) image, respectively.
• Hyperspectral Pansharpening: Critical Review, Tools and Future Perspectives
  1 Jul 2024 1 repository listed
  In part, this comes from the technical complexity of the task: compared to multispectral pansharpening, many more bands are involved, in a spectral range only partially covered by the panchromatic component and with…
• Deep Unfolding Tensor Rank Minimization With Generalized Detail Injection for Pansharpening
  22 Apr 2024 1 repository listed
  Finally, the iterative algorithm is unfolded into a multistage deep network, in which the optimization variables are solved by closed-form solutions and a data-driven regularizer in each stage.
• A Novel State Space Model with Local Enhancement and State Sharing for Image Fusion
  14 Apr 2024 1 repository listed
  The LEVM block can improve local information perception of the network and simultaneously learn local and global spatial information.
• Content-Adaptive Non-Local Convolution for Remote Sensing Pansharpening
  11 Apr 2024 1 repository listed Syntology ran 3 of 4 samples · 1 unverified · 4 pointer-only (licence)
  In this paper, we introduce a so-called content-adaptive non-local convolution (CANConv), a novel method tailored for remote sensing image pansharpening.
• Perspective-Equivariance for Unsupervised Imaging with Camera Geometry
  14 Mar 2024 1 repository listed
  We show that our much richer non-linear class of group transforms, derived from camera geometry, generalises previous EI work and is an excellent prior for satellite and urban image data.
• Pan-Mamba: Effective pan-sharpening with State Space Model
  19 Feb 2024 1 repository listed Syntology ran 12 of 13 samples · 1 unverified · 13 pointer-only (licence)
  To the best of our knowledge, this work is the first attempt in exploring the potential of the Mamba model and establishes a new frontier in the pan-sharpening techniques.
• Band-wise Hyperspectral Image Pansharpening using CNN Model Propagation
  11 Nov 2023 1 repository listed
  Hyperspectral pansharpening is receiving a growing interest since the last few years as testified by a large number of research papers and challenges.
• Unsupervised Hyperspectral Pansharpening via Low-rank Diffusion Model
  18 May 2023 1 repository listed
  To address these issues, in this work, we propose a low-rank diffusion model for hyperspectral pansharpening by simultaneously leveraging the power of the pre-trained deep diffusion model and better generalization…
• Local-Global Transformer Enhanced Unfolding Network for Pan-sharpening
  28 Apr 2023 1 repository listed
  Moreover, we customize a Local-Global Transformer (LGT) to simultaneously model local and global dependencies, and further formulate an LGT-based prior module for image denoising.
• Probability-based Global Cross-modal Upsampling for Pansharpening
  9 Mar 2023 1 repository listed Syntology ran 1 of 2 samples · 1 unverified
  Pansharpening is an essential preprocessing step for remote sensing image processing.
• Fast Full-Resolution Target-Adaptive CNN-Based Pansharpening Framework
  5 Jan 2023 1 repository listed
  In this work, we propose a variant of this method with an effective target-adaptation scheme that allows for the reduction in inference time by a factor of ten, on average, without accuracy loss.
• U2Net: A General Framework with Spatial-Spectral-Integrated Double U-Net for Image Fusion
  13 Dec 2022 1 repository listed
  The U2Net utilizes a spatial U-Net and a spectral U-Net to extract spatial details and spectral characteristics, which allows for the discriminative and hierarchical learning of features from diverse images.
• DiTBN: Detail Injection-Based Two-Branch Network for Pansharpening of Remote Sensing Images
  2 Dec 2022 1 repository listed
  Pansharpening is one of the main research topics in the field of remote sensing image processing.
• Pansharpening via Frequency-Aware Fusion Network with Explicit Similarity Constraints
  18 Jul 2022 1 repository listed
  The process of fusing a high spatial resolution (HR) panchromatic (PAN) image and a low spatial resolution (LR) multispectral (MS) image to obtain an HRMS image is known as pansharpening.
• HyperTransformer: A Textural and Spectral Feature Fusion Transformer for Pansharpening
  4 Mar 2022 1 repository listed
  Existing pansharpening approaches neglect using an attention mechanism to transfer HR texture features from PAN to LR-HSI features, resulting in spatial and spectral distortions.
• Full-resolution quality assessment for pansharpening
  13 Aug 2021 1 repository listed
  Both reference-based and no-reference indexes present critical shortcomings which motivate the community to explore new solutions.
• Hyperspectral Pansharpening Based on Improved Deep Image Prior and Residual Reconstruction
  6 Jul 2021 1 repository listed
  To estimate the PAN image of the up-sampled HSI, we also propose a learnable spectral response function (SRF).
• Sentinel-2 Sharpening Using a Single Unsupervised Convolutional Neural Network With MTF-Based Degradation Model
  24 Jun 2021 1 repository listed
  However, the downside of those methods is that the DL-based methods need to be trained separately for the 20 m and the 60 m bands in a supervised manner at reduced resolution, while the model-based methods heavily…

Syntology lines on 5 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.