# VL-UR: Vision-Language-guided Universal Restoration of Images Degraded by Adverse Weather Conditions

arXiv: https://arxiv.org/abs/2504.08219 · HTML: https://mlsanity.com/paper/2504.08219/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 21 references that are in the map.
- First posted on arXiv 11 Apr 2025.

## Builds on (most cited first, 9 of 9)

- [Exploring CLIP for Assessing the Look and Feel of Images](https://mlsanity.com/paper/2207.12396/) (2022)
- [Pre-Trained Image Processing Transformer](https://mlsanity.com/paper/2012.00364/) (2020)
- [TransWeather: Transformer-based Restoration of Images Degraded by Adverse Weather Conditions](https://mlsanity.com/paper/2111.14813/) (2021)
- [Restoring Vision in Adverse Weather Conditions with Patch-Based Denoising Diffusion Models](https://mlsanity.com/paper/2207.14626/) (2022)
- [Learning Enriched Features for Fast Image Restoration and Enhancement](https://mlsanity.com/paper/2205.01649/) (2022)
- [Iterative Prompt Learning for Unsupervised Backlit Image Enhancement](https://mlsanity.com/paper/2303.17569/) (2023)
- [OneRestore: A Universal Restoration Framework for Composite Degradation](https://mlsanity.com/paper/2407.04621/) (2024)
- [Improving Image Restoration through Removing Degradations in Textual Representations](https://mlsanity.com/paper/2312.17334/) (2023)
- [TextIR: A Simple Framework for Text-based Editable Image Restoration](https://mlsanity.com/paper/2302.14736/) (2023)

## Built on by (most cited first, 2 of 2)

- [Breaking Degradation Coupling: A Structural Entropy Guided Decoupled Framework and Benchmark for Infrared Enhancement](https://mlsanity.com/paper/2604.22886/) (2026)
- [Expandable, Compressible, Mineable: Open-World Thermal Image Restoration](https://mlsanity.com/paper/2605.16967/) (2026)

## Abstract (arXiv)

Image restoration is critical for improving the quality of degraded images, which is vital for applications like autonomous driving, security surveillance, and digital content enhancement. However, existing methods are often tailored to specific degradation scenarios, limiting their adaptability to the diverse and complex challenges in real-world environments. Moreover, real-world degradations are typically non-uniform, highlighting the need for adaptive and intelligent solutions. To address these issues, we propose a novel vision-language-guided universal restoration (VL-UR) framework. VL-UR leverages a zero-shot contrastive language-image pre-training (CLIP) model to enhance image restoration by integrating visual and semantic information. A scene classifier is introduced to adapt CLIP, generating high-quality language embeddings aligned with degraded images while predicting degraded types for complex scenarios. Extensive experiments across eleven diverse degradation settings demonstrate VL-UR's state-of-the-art performance, robustness, and adaptability. This positions VL-UR as a transformative solution for modern image restoration challenges in dynamic, real-world environments.