Degradation-Aware Image Enhancement via Vision-Language Classification
Journal:
arXiv
Published Date:
Jun 5, 2025
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.
