  Image denoising is a fundamental task in computer vision. While prevailing
deep learning-based supervised and self-supervised methods have excelled in
eliminating in-distribution noise, their susceptibility to out-of-distribution
(OOD) noise remains a significant challenge. The recent emergence of
contrastive language-image pre-training (CLIP) model has showcased exceptional
capabilities in open-world image recognition and segmentation. Yet, the
potential for leveraging CLIP to enhance the robustness of low-level tasks
remains largely unexplored. This paper uncovers that certain dense features
extracted from the frozen ResNet image encoder of CLIP exhibit
distortion-invariant and content-related properties, which are highly desirable
for generalizable denoising. Leveraging these properties, we devise an
asymmetrical encoder-decoder denoising network, which incorporates dense
features including the noisy image and its multi-scale features from the frozen
ResNet encoder of CLIP into a learnable image decoder to achieve generalizable
denoising. The progressive feature augmentation strategy is further proposed to
mitigate feature overfitting and improve the robustness of the learnable
decoder. Extensive experiments and comparisons conducted across diverse OOD
noises, including synthetic noise, real-world sRGB noise, and low-dose CT image
noise, demonstrate the superior generalization ability of our method.
