Degradation-Aware Image Enhancement via Vision-Language Classification

TL;DR

This paper introduces a framework that uses vision-language models to classify degraded images into categories and applies targeted restoration techniques, significantly improving image quality in real-world scenarios.

Contribution

The novel integration of vision-language classification with specialized restoration models for different degradation types.

Findings

Accurate classification of four degradation types.

Effective restoration of degraded images.

Scalable and automated image enhancement pipeline.

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.