Degradation-Aware Image Enhancement via Vision-Language Classification
This provides an automated solution for real-world image enhancement tasks, but it is incremental as it combines existing VLMs and restoration techniques.
The paper tackles the problem of image degradation by proposing a framework that uses a Vision-Language Model to classify images into degradation types and applies targeted restoration, resulting in improved visual quality through accurate classification and specialized models.
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.