# Content-Adaptive Non-Local Convolution for Remote Sensing Pansharpening

arXiv: https://arxiv.org/abs/2404.07543 · HTML: https://mlsanity.com/paper/2404.07543/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 49 references that are in the map.
- First posted on arXiv 11 Apr 2024.

## Builds on (most cited first, 13 of 13)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Deformable Convolutional Networks](https://mlsanity.com/paper/1703.06211/) (2017)
- [Deformable ConvNets v2: More Deformable, Better Results](https://mlsanity.com/paper/1811.11168/) (2018)
- [Dynamic Convolution: Attention over Convolution Kernels](https://mlsanity.com/paper/1912.03458/) (2019)
- [Dynamic Filter Networks](https://mlsanity.com/paper/1605.09673/) (2016)
- [Pixel-Adaptive Convolutional Neural Networks](https://mlsanity.com/paper/1904.05373/) (2019)
- [Involution: Inverting the Inherence of Convolution for Visual Recognition](https://mlsanity.com/paper/2103.06255/) (2021)
- [Neural Nearest Neighbors Networks](https://mlsanity.com/paper/1810.12575/) (2018)
- [Cross-Scale Internal Graph Neural Network for Image Super-Resolution](https://mlsanity.com/paper/2006.16673/) (2020)
- [Decoupled Dynamic Filter Networks](https://mlsanity.com/paper/2104.14107/) (2021)
- [Dynamic Region-Aware Convolution](https://mlsanity.com/paper/2003.12243/) (2020)
- [Pansharpening via Detail Injection Based Convolutional Neural Networks](https://mlsanity.com/paper/1806.08898/) (2018)

## Built on by (most cited first, 20 of 20)

- [Adaptive Rectangular Convolution for Remote Sensing Pansharpening](https://mlsanity.com/paper/2503.00467/) (2025)
- [U-Know-DiffPAN: An Uncertainty-aware Knowledge Distillation Diffusion Framework with Details Enhancement for PAN-Sharpening](https://mlsanity.com/paper/2412.06243/) (2024)
- [PAN-Crafter: Learning Modality-Consistent Alignment for PAN-Sharpening](https://mlsanity.com/paper/2505.23367/) (2025)
- [When Images Look Right and Retrieve Wrong: Coverage-Guided Cross-Scale Re-Indexing for Knowledge-Faithful Generative Perception](https://mlsanity.com/paper/2608.20810/) (2026)
- [Multi-Head Attention Residual Unfolded Network for Model-Based Pansharpening](https://mlsanity.com/paper/2409.02675/) (2024)
- [SACB-Net: Spatial-awareness Convolutions for Medical Image Registration](https://mlsanity.com/paper/2503.19592/) (2025)
- [Pan-LUT: Efficient Pan-sharpening via Learnable Look-Up Tables](https://mlsanity.com/paper/2503.23793/) (2025)
- [Enpowering Your Pansharpening Models with Generalizability: Unified Distribution is All You Need](https://mlsanity.com/paper/2510.22217/) (2025)
- [Two-Stage Random Alternation Framework for One-Shot Pansharpening](https://mlsanity.com/paper/2505.06576/) (2025)
- [Fast Kernel-Space Diffusion for Remote Sensing Pansharpening](https://mlsanity.com/paper/2505.18991/) (2025)
- [RAPNet: A Receptive-Field Adaptive Convolutional Neural Network for Pansharpening](https://mlsanity.com/paper/2507.10461/) (2025)
- [Spatial-Spectral Binarized Neural Network for Panchromatic and Multi-spectral Images Fusion](https://mlsanity.com/paper/2509.23321/) (2025)
- [SatFusion: A Unified Framework for Enhancing Remote Sensing Images via Multi-Frame and Multi-Source Images Fusion](https://mlsanity.com/paper/2510.07905/) (2025)
- [Robust Non-negative Proximal Gradient Algorithm for Inverse Problems](https://mlsanity.com/paper/2510.23362/) (2025)
- [DMAConv: Dual Mask-Adaptive Convolution for Remote Sensing Pansharpening](https://mlsanity.com/paper/2512.08331/) (2025)
- [Robust Self-Supervised Cross-Modal Super-Resolution against Real-World Misaligned Observations](https://mlsanity.com/paper/2602.18822/) (2026)
- [Universal Pansharpening Model](https://mlsanity.com/paper/2603.03831/) (2026)
- [CGFformer: Cluster-Guidance Frequency Transformer for Pansharpening](https://mlsanity.com/paper/2605.01490/) (2026)
- [RAFNet: Region-Aware Fusion Network for Pansharpening](https://mlsanity.com/paper/2605.02184/) (2026)
- [Adaptive 3D Convolution for Remote Sensing Image Fusion](https://mlsanity.com/paper/2605.09455/) (2026)

## Abstract (arXiv)

Currently, machine learning-based methods for remote sensing pansharpening have progressed rapidly. However, existing pansharpening methods often do not fully exploit differentiating regional information in non-local spaces, thereby limiting the effectiveness of the methods and resulting in redundant learning parameters. In this paper, we introduce a so-called content-adaptive non-local convolution (CANConv), a novel method tailored for remote sensing image pansharpening. Specifically, CANConv employs adaptive convolution, ensuring spatial adaptability, and incorporates non-local self-similarity through the similarity relationship partition (SRP) and the partition-wise adaptive convolution (PWAC) sub-modules. Furthermore, we also propose a corresponding network architecture, called CANNet, which mainly utilizes the multi-scale self-similarity. Extensive experiments demonstrate the superior performance of CANConv, compared with recent promising fusion methods. Besides, we substantiate the method's effectiveness through visualization, ablation experiments, and comparison with existing methods on multiple test sets. The source code is publicly available at https://github.com/duanyll/CANConv.