Degradation-Aware Image Enhancement via Vision-Language Classification

Image degradation is a prevalent issue in various real-world applications, affecting visual quality and downstream processing tasks. In this study, we propose a novel framework that employs a Vision-Language Model (VLM) to automatically classify degraded images into predefined categories. The VLM categorizes an input image into one of four degradation types: (A) super-resolution degradation (including noise, blur, and JPEG compression), (B) reflection artifacts, (C) motion blur, or (D) no visible degradation (high-quality image). Once classified, images assigned to categories A, B, or C undergo targeted restoration using dedicated models tailored for each specific degradation type. The final output is a restored image with improved visual quality. Experimental results demonstrate the effectiveness of our approach in accurately classifying image degradations and enhancing image quality through specialized restoration models. Our method presents a scalable and automated solution for real-world image enhancement tasks, leveraging the capabilities of VLMs in conjunction with state-of-the-art restoration techniques.

@article{cai2025_2506.05450,
  title={ Degradation-Aware Image Enhancement via Vision-Language Classification },
  author={ Jie Cai and Kangning Yang and Jiaming Ding and Lan Fu and Ling Ouyang and Jiang Li and Jinglin Shen and Zibo Meng },
  journal={arXiv preprint arXiv:2506.05450},
  year={ 2025 }
}