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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2506.05450 |
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| _version_ | 1866913878873473024 |
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| author | Cai, Jie Yang, Kangning Ding, Jiaming Fu, Lan Ouyang, Ling Li, Jiang Shen, Jinglin Meng, Zibo |
| author_facet | Cai, Jie Yang, Kangning Ding, Jiaming Fu, Lan Ouyang, Ling Li, Jiang Shen, Jinglin Meng, Zibo |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_05450 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Degradation-Aware Image Enhancement via Vision-Language Classification Cai, Jie Yang, Kangning Ding, Jiaming Fu, Lan Ouyang, Ling Li, Jiang Shen, Jinglin Meng, Zibo Computer Vision and Pattern Recognition 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. |
| title | Degradation-Aware Image Enhancement via Vision-Language Classification |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2506.05450 |