  Image restoration aims to recover the high-quality images from their degraded
observations. Since most existing methods have been dedicated into single
degradation removal, they may not yield optimal results on other types of
degradations, which do not satisfy the applications in real world scenarios. In
this paper, we propose a novel data ingredient-oriented approach that leverages
prompt-based learning to enable a single model to efficiently tackle multiple
image degradation tasks. Specifically, we utilize a encoder to capture features
and introduce prompts with degradation-specific information to guide the
decoder in adaptively recovering images affected by various degradations. In
order to model the local invariant properties and non-local information for
high-quality image restoration, we combined CNNs operations and Transformers.
Simultaneously, we made several key designs in the Transformer blocks
(multi-head rearranged attention with prompts and simple-gate feed-forward
network) to reduce computational requirements and selectively determines what
information should be persevered to facilitate efficient recovery of
potentially sharp images. Furthermore, we incorporate a feature fusion
mechanism further explores the multi-scale information to improve the
aggregated features. The resulting tightly interlinked hierarchy architecture,
named as CAPTNet, extensive experiments demonstrate that our method performs
competitively to the state-of-the-art.
