# Building Bridges across Spatial and Temporal Resolutions: Reference-Based Super-Resolution via Change Priors and Conditional Diffusion Model arXiv: https://arxiv.org/abs/2403.17460 ยท HTML: https://mlsanity.com/paper/2403.17460/ ## Key facts - Cited by 20 papers in the map (data through 14 Oct 2026). - Placed in Inverse problems and image restoration, one of 14 directions in Diffusion models. - Builds on 32 of its 58 references that are in the map. - First posted on arXiv 26 Mar 2024. ## Placed in - Diffusion models / [Inverse problems and image restoration](https://mlsanity.com/diffusion/inverse-problems-and-image-restoration/) ## Builds on (most cited first, 30 of 32) - [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017) - [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018) - [Elucidating the Design Space of Diffusion-Based Generative Models](https://mlsanity.com/paper/2206.00364/) (2022) - [Image Super-Resolution via Iterative Refinement](https://mlsanity.com/paper/2104.07636/) (2021) - [Semantic Image Synthesis with Spatially-Adaptive Normalization](https://mlsanity.com/paper/1903.07291/) (2019) - [Cascaded Diffusion Models for High Fidelity Image Generation](https://mlsanity.com/paper/2106.15282/) (2021) - [Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data](https://mlsanity.com/paper/2107.10833/) (2021) - [Exploiting Diffusion Prior for Real-World Image Super-Resolution](https://mlsanity.com/paper/2305.07015/) (2023) - [SRDiff: Single Image Super-Resolution with Diffusion Probabilistic Models](https://mlsanity.com/paper/2104.14951/) (2021) - [Recovering Realistic Texture in Image Super-resolution by Deep Spatial Feature Transform](https://mlsanity.com/paper/1804.02815/) (2018) - [Learning Texture Transformer Network for Image Super-Resolution](https://mlsanity.com/paper/2006.04139/) (2020) - [DiffBIR: Towards Blind Image Restoration with Generative Diffusion Prior](https://mlsanity.com/paper/2308.15070/) (2023) - [DiffIR: Efficient Diffusion Model for Image Restoration](https://mlsanity.com/paper/2303.09472/) (2023) - [Pixel-Aware Stable Diffusion for Realistic Image Super-resolution and Personalized Stylization](https://mlsanity.com/paper/2308.14469/) (2023) - [A Transformer-Based Siamese Network for Change Detection](https://mlsanity.com/paper/2201.01293/) (2022) - [Implicit Diffusion Models for Continuous Super-Resolution](https://mlsanity.com/paper/2303.16491/) (2023) - [Blind Super-Resolution With Iterative Kernel Correction](https://mlsanity.com/paper/1904.03377/) (2019) - [Semantic Image Synthesis via Diffusion Models](https://mlsanity.com/paper/2207.00050/) (2022) - [MASA-SR: Matching Acceleration and Spatial Adaptation for Reference-Based Image Super-Resolution](https://mlsanity.com/paper/2106.02299/) (2021) - [DynamicEarthNet: Daily Multi-Spectral Satellite Dataset for Semantic Change Segmentation](https://mlsanity.com/paper/2203.12560/) (2022) - [Robust Reference-based Super-Resolution via C2-Matching](https://mlsanity.com/paper/2106.01863/) (2021) - [ResDiff: Combining CNN and Diffusion Model for Image Super-Resolution](https://mlsanity.com/paper/2303.08714/) (2023) - [Reference-based Image Super-Resolution with Deformable Attention Transformer](https://mlsanity.com/paper/2207.11938/) (2022) - [Semantic Change Detection with Asymmetric Siamese Networks](https://mlsanity.com/paper/2010.05687/) (2020) - [HSR-Diff:Hyperspectral Image Super-Resolution via Conditional Diffusion Models](https://mlsanity.com/paper/2306.12085/) (2023) - [Super-resolution-based Change Detection Network with Stacked Attention Module for Images with Different Resolutions](https://mlsanity.com/paper/2103.00188/) (2021) - [Coarse-to-Fine Embedded PatchMatch and Multi-Scale Dynamic Aggregation for Reference-based Super-Resolution](https://mlsanity.com/paper/2201.04358/) (2022) - [ACDMSR: Accelerated Conditional Diffusion Models for Single Image Super-Resolution](https://mlsanity.com/paper/2307.00781/) (2023) - [RRSR:Reciprocal Reference-based Image Super-Resolution with Progressive Feature Alignment and Selection](https://mlsanity.com/paper/2211.04203/) (2022) - [Dual-Diffusion: Dual Conditional Denoising Diffusion Probabilistic Models for Blind Super-Resolution Reconstruction in RSIs](https://mlsanity.com/paper/2305.12170/) (2023) ## Built on by (most cited first, 20 of 20) - [Diffusion Models Meet Remote Sensing: Principles, Methods, and Perspectives](https://mlsanity.com/paper/2404.08926/) (2024) - [Semantic Guided Large Scale Factor Remote Sensing Image Super-resolution with Generative Diffusion Prior](https://mlsanity.com/paper/2405.07044/) (2024) - [A2-MAE: A spatial-temporal-spectral unified remote sensing pre-training method based on anchor-aware masked autoencoder](https://mlsanity.com/paper/2406.08079/) (2024) - [Can Generative Geospatial Diffusion Models Excel as Discriminative Geospatial Foundation Models?](https://mlsanity.com/paper/2503.07890/) (2025) - [ChangeAnywhere: Sample Generation for Remote Sensing Change Detection via Semantic Latent Diffusion Model](https://mlsanity.com/paper/2404.08892/) (2024) - [Spiking Meets Attention: Efficient Remote Sensing Image Super-Resolution with Attention Spiking Neural Networks](https://mlsanity.com/paper/2503.04223/) (2025) - [RS-Prune: Training-Free Data Pruning at High Ratios for Efficient Remote Sensing Diffusion Foundation Models](https://mlsanity.com/paper/2512.23239/) (2025) - [NeurOp-Diff:Continuous Remote Sensing Image Super-Resolution via Neural Operator Diffusion](https://mlsanity.com/paper/2501.09054/) (2025) - [Semantic-Guided Cross-Sensor Super Resolution of Remote Sensing Images: A Gated Dual Conditioning Flow Matching Model](https://mlsanity.com/paper/2510.23816/) (2025) - [COP-GEN-Beta: Unified Generative Modelling of COPernicus Imagery Thumbnails](https://mlsanity.com/paper/2504.08548/) (2025) - [HiTSR: A Hierarchical Transformer for Reference-based Super-Resolution](https://mlsanity.com/paper/2408.16959/) (2024) - [A Gift from the Integration of Discriminative and Diffusion-based Generative Learning: Boundary Refinement Remote Sensing Semantic Segmentation](https://mlsanity.com/paper/2507.01573/) (2025) - [Equivariant Sampling for Improving Diffusion Model-based Image Restoration](https://mlsanity.com/paper/2511.09965/) (2025) - [COP-GEN: Latent Diffusion Transformer for Copernicus Earth Observation Data](https://mlsanity.com/paper/2603.03239/) (2026) - [GDSR: Global-Detail Integration through Dual-Branch Network with Wavelet Losses for Remote Sensing Image Super-Resolution](https://mlsanity.com/paper/2501.01460/) (2024) - [TTRD3: Texture Transfer Residual Denoising Dual Diffusion Model for Remote Sensing Image Super-Resolution](https://mlsanity.com/paper/2504.13026/) (2025) - [Controllable Reference Guided Diffusion with Local Global Fusion for Real World Remote Sensing Image Super Resolution](https://mlsanity.com/paper/2506.23801/) (2025) - [Segmentation-Aware Latent Diffusion for Satellite Image Super-Resolution: Enabling Smallholder Farm Boundary Delineation](https://mlsanity.com/paper/2511.14481/) (2025) - [RefSR-Adv: Adversarial Attack on Reference-based Image Super-Resolution Models](https://mlsanity.com/paper/2601.01202/) (2026) - [Learning to Balance: Decoupled Siamese Diffusion Transformer for Reference-Based Remote Sensing Image Super-Resolution](https://mlsanity.com/paper/2605.17980/) (2026) ## Abstract (arXiv) Reference-based super-resolution (RefSR) has the potential to build bridges across spatial and temporal resolutions of remote sensing images. However, existing RefSR methods are limited by the faithfulness of content reconstruction and the effectiveness of texture transfer in large scaling factors. Conditional diffusion models have opened up new opportunities for generating realistic high-resolution images, but effectively utilizing reference images within these models remains an area for further exploration. Furthermore, content fidelity is difficult to guarantee in areas without relevant reference information. To solve these issues, we propose a change-aware diffusion model named Ref-Diff for RefSR, using the land cover change priors to guide the denoising process explicitly. Specifically, we inject the priors into the denoising model to improve the utilization of reference information in unchanged areas and regulate the reconstruction of semantically relevant content in changed areas. With this powerful guidance, we decouple the semantics-guided denoising and reference texture-guided denoising processes to improve the model performance. Extensive experiments demonstrate the superior effectiveness and robustness of the proposed method compared with state-of-the-art RefSR methods in both quantitative and qualitative evaluations. The code and data are available at https://github.com/dongrunmin/RefDiff.