  How to explore useful features from images as prompts to guide the deep image
restoration models is an effective way to solve image restoration. In contrast
to mining spatial relations within images as prompt, which leads to
characteristics of different frequencies being neglected and further remaining
subtle or undetectable artifacts in the restored image, we develop a Frequency
Prompting image restoration method, dubbed FPro, which can effectively provide
prompt components from a frequency perspective to guild the restoration model
address these differences. Specifically, we first decompose input features into
separate frequency parts via dynamically learned filters, where we introduce a
gating mechanism for suppressing the less informative elements within the
kernels. To propagate useful frequency information as prompt, we then propose a
dual prompt block, consisting of a low-frequency prompt modulator (LPM) and a
high-frequency prompt modulator (HPM), to handle signals from different bands
respectively. Each modulator contains a generation process to incorporate
prompting components into the extracted frequency maps, and a modulation part
that modifies the prompt feature with the guidance of the decoder features.
Experimental results on commonly used benchmarks have demonstrated the
favorable performance of our pipeline against SOTA methods on 5 image
restoration tasks, including deraining, deraindrop, demoir\'eing, deblurring,
and dehazing. The source code and pre-trained models will be available at
https://github.com/joshyZhou/FPro.
