Degradation-Aware Image Enhancement via Vision-Language Classification

2025-06-05

AI Summary

Real-world image degradations are diverse and challenging to identify automatically. Method: This paper pioneers the integration of vision-language models (VLMs) into degradation classification, proposing a fine-grained degradation-aware and modular restoration framework. It categorizes degradations into four types—super-resolution-related, reflection, motion blur, and no degradation—and employs a VLM to accurately classify each input image, subsequently activating a dedicated reconstruction network for on-demand enhancement. Domain adaptation is incorporated to improve cross-scenario generalization. Results: On multiple benchmark datasets, the method achieves 92.7% degradation classification accuracy and significantly outperforms unified enhancement approaches in PSNR and SSIM. It also enhances perceptual quality of restored images and improves performance on downstream tasks, thereby overcoming inherent limitations of end-to-end models in interpretability and generalizability.

Technology Category

• Computer Vision: Large Vision Models
• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Language Grounding & Multi-modal NLP

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

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.

Problem

• Classify image degradation types using Vision-Language Model
• Restore degraded images with targeted models for each type
• Improve visual quality via scalable automated enhancement framework

Innovation

• Uses Vision-Language Model for degradation classification
• Applies dedicated models for specific degradation types
• Automates scalable image enhancement via VLM