Degradation-Aware Image Enhancement via Vision-Language Classification

• URL: http://arxiv.org/abs/2506.05450v1
• Date: Thu, 05 Jun 2025 17:42:01 GMT
• Title: Degradation-Aware Image Enhancement via Vision-Language Classification
• Authors: Jie Cai, Kangning Yang, Jiaming Ding, Lan Fu, Ling Ouyang, Jiang Li, Jinglin Shen, Zibo Meng,
• Abstract summary: We propose a framework that employs a Vision-Language Model (VLM) to automatically classify degraded images into predefined categories.<n>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.<n>Once classified, images assigned to categories A, B, or C undergo targeted restoration using dedicated models tailored for each specific degradation type.
• Score: 12.72311942967158
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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