  Pre-trained models with large-scale training data, such as CLIP and Stable
Diffusion, have demonstrated remarkable performance in various high-level
computer vision tasks such as image understanding and generation from language
descriptions. Yet, their potential for low-level tasks such as image
restoration remains relatively unexplored. In this paper, we explore such
models to enhance image restoration. As off-the-shelf features (OSF) from
pre-trained models do not directly serve image restoration, we propose to learn
an additional lightweight module called Pre-Train-Guided Refinement Module
(PTG-RM) to refine restoration results of a target restoration network with
OSF. PTG-RM consists of two components, Pre-Train-Guided Spatial-Varying
Enhancement (PTG-SVE), and Pre-Train-Guided Channel-Spatial Attention
(PTG-CSA). PTG-SVE enables optimal short- and long-range neural operations,
while PTG-CSA enhances spatial-channel attention for restoration-related
learning. Extensive experiments demonstrate that PTG-RM, with its compact size
($<$1M parameters), effectively enhances restoration performance of various
models across different tasks, including low-light enhancement, deraining,
deblurring, and denoising.
