LLMRA: Multi-modal Large Language Model based Restoration Assistant
Xiaoyu Jin Yuan Shi Bin Xia Wenming Yang
Multi-modal Large Language Models (MLLMs) have a significant impact on
various tasks, due to their extensive knowledge and powerful perception and
generation capabilities. However, it still remains an open research problem on
applying MLLMs to low-level vision tasks. In this paper, we present a simple
MLLM-based Image Restoration framework to address this gap, namely Multi-modal
Large Language Model based Restoration Assistant (LLMRA). We exploit the
impressive capabilities of MLLMs to obtain the degradation information for
universal image restoration. By employing a pretrained multi-modal large
language model and a vision language model, we generate text descriptions and
encode them as context embedding with degradation information for the degraded
image. Through the proposed Context Enhance Module (CEM) and Degradation
Context based Transformer Network (DC-former), we integrate these context
embedding into the restoration network, contributing to more accurate and
adjustable image restoration. Based on the dialogue with the users, our method
leverages image degradation priors from MLLMs, providing low-level attributes
descriptions of the input low-quality images and the restored high-quality
images simultaneously. Extensive experiments demonstrate the superior
performance of our LLMRA in universal image restoration tasks.