# DC4CR: When Cloud Removal Meets Diffusion Control in Remote Sensing

arXiv: https://arxiv.org/abs/2504.14785 · HTML: https://mlsanity.com/paper/2504.14785/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 37 references that are in the map.
- First posted on arXiv 21 Apr 2025.

## Builds on (most cited first, 18 of 18)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding](https://mlsanity.com/paper/2205.11487/) (2022)
- [Adding Conditional Control to Text-to-Image Diffusion Models](https://mlsanity.com/paper/2302.05543/) (2023)
- [DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation](https://mlsanity.com/paper/2208.12242/) (2022)
- [Video Diffusion Models](https://mlsanity.com/paper/2204.03458/) (2022)
- [Imagic: Text-Based Real Image Editing with Diffusion Models](https://mlsanity.com/paper/2210.09276/) (2022)
- [Pixel-Aware Stable Diffusion for Realistic Image Super-resolution and Personalized Stylization](https://mlsanity.com/paper/2308.14469/) (2023)
- [FreeU: Free Lunch in Diffusion U-Net](https://mlsanity.com/paper/2309.11497/) (2023)
- [SEN12MS-CR-TS: A Remote Sensing Data Set for Multi-modal Multi-temporal Cloud Removal](https://mlsanity.com/paper/2201.09613/) (2022)
- [A Remote Sensing Image Dataset for Cloud Removal](https://mlsanity.com/paper/1901.00600/) (2019)
- [Cloud Removal for Remote Sensing Imagery via Spatial Attention Generative Adversarial Network](https://mlsanity.com/paper/2009.13015/) (2020)
- [Diffusion Enhancement for Cloud Removal in Ultra-Resolution Remote Sensing Imagery](https://mlsanity.com/paper/2401.15105/) (2024)
- [IDF-CR: Iterative Diffusion Process for Divide-and-Conquer Cloud Removal in Remote-sensing Images](https://mlsanity.com/paper/2403.11870/) (2024)
- [Yuan: Yielding Unblemished Aesthetics Through A Unified Network for Visual Imperfections Removal in Generated Images](https://mlsanity.com/paper/2501.08505/) (2025)
- [Memory augment is All You Need for image restoration](https://mlsanity.com/paper/2309.01377/) (2023)

## Built on by (most cited first, 4 of 4)

- [From Physics to Foundation Models: A Review of AI-Driven Quantitative Remote Sensing Inversion](https://mlsanity.com/paper/2507.09081/) (2025)
- [Pansharpening for Thin-Cloud Contaminated Remote Sensing Images: A Unified Framework and Benchmark Dataset](https://mlsanity.com/paper/2603.14952/) (2026)
- [Multi-Modal Machine Learning Framework for Predicting Early Recurrence of Brain Tumors Using MRI and Clinical Biomarkers](https://mlsanity.com/paper/2509.01161/) (2025)
- [A Multimodal Deep Learning Framework for Early Diagnosis of Liver Cancer via Optimized BiLSTM-AM-VMD Architecture](https://mlsanity.com/paper/2509.01164/) (2025)

## Abstract (arXiv)

Cloud occlusion significantly hinders remote sensing applications by obstructing surface information and complicating analysis. To address this, we propose DC4CR (Diffusion Control for Cloud Removal), a novel multimodal diffusion-based framework for cloud removal in remote sensing imagery. Our method introduces prompt-driven control, allowing selective removal of thin and thick clouds without relying on pre-generated cloud masks, thereby enhancing preprocessing efficiency and model adaptability. Additionally, we integrate low-rank adaptation for computational efficiency, subject-driven generation for improved generalization, and grouped learning to enhance performance on small datasets. Designed as a plug-and-play module, DC4CR seamlessly integrates into existing cloud removal models, providing a scalable and robust solution. Extensive experiments on the RICE and CUHK-CR datasets demonstrate state-of-the-art performance, achieving superior cloud removal across diverse conditions. This work presents a practical and efficient approach for remote sensing image processing with broad real-world applications.