  Image restoration has always been a cutting-edge topic in the academic and
industrial fields of computer vision. Since degradation signals are often
random and diverse, "all-in-one" models that can do blind image restoration
have been concerned in recent years. Early works require training specialized
headers and tails to handle each degradation of concern, which are manually
cumbersome. Recent works focus on learning visual prompts from data
distribution to identify degradation type. However, the prompts employed in
most of models are non-text, lacking sufficient emphasis on the importance of
human-in-the-loop. In this paper, an effective textual prompt guided image
restoration model has been proposed. In this model, task-specific BERT is
fine-tuned to accurately understand user's instructions and generating textual
prompt guidance. Depth-wise multi-head transposed attentions and gated
convolution modules are designed to bridge the gap between textual prompts and
visual features. The proposed model has innovatively introduced semantic
prompts into low-level visual domain. It highlights the potential to provide a
natural, precise, and controllable way to perform image restoration tasks.
Extensive experiments have been done on public denoising, dehazing and
deraining datasets. The experiment results demonstrate that, compared with
popular state-of-the-art methods, the proposed model can obtain much more
superior performance, achieving accurate recognition and removal of degradation
without increasing model's complexity. Related source codes and data will be
publicly available on github site
https://github.com/MoTong-AI-studio/TextPromptIR.
