Rethinking Image Deraining via Text-guided Detail Reconstruction

Image deraining aims to recover clean images from degradation caused by rain streaks or raindrops of varying intensities. Recently many learning-based approaches have been proposed and achieved promising performance. However, these methods either focus on network architecture design or solely introduce image-level prior to the model. In this paper, we introduce text prior assisting the model in image deraining, as text descriptions have high flexibility and scalability. Text prior provides a wealth of semantic information to help the model achieve more detailed restoration, rather than blindly extrapolating details lost in the degraded image. To this end, we propose a novel image deraining framework based on the transformer, named TGDeraining. Specifically, to incorporate text prior into the framework, we design the Text Prior Embedded Transformer Block (TETB). TETB allows for dynamic guidance of the attention map guided by the text descriptions, thus emphasizing the restoration of critical missing details. The text prior is also fed to the feed-forward network to transform features in a controlled manner. Extensive experimental results demonstrate the effectiveness of our method in restoring a clear image using text as reference information.