  Image processing, including image restoration, image enhancement, etc.,
involves generating a high-quality clean image from a degraded input. Deep
learning-based methods have shown superior performance for various image
processing tasks in terms of single-task conditions. However, they require to
train separate models for different degradations and levels, which limits the
generalization abilities of these models and restricts their applications in
real-world. In this paper, we propose a text-induced unified image processor
for low-level vision tasks, termed UniProcessor, which can effectively process
various degradation types and levels, and support multimodal control.
Specifically, our UniProcessor encodes degradation-specific information with
the subject prompt and process degradations with the manipulation prompt. These
context control features are injected into the UniProcessor backbone via
cross-attention to control the processing procedure. For automatic
subject-prompt generation, we further build a vision-language model for
general-purpose low-level degradation perception via instruction tuning
techniques. Our UniProcessor covers 30 degradation types, and extensive
experiments demonstrate that our UniProcessor can well process these
degradations without additional training or tuning and outperforms other
competing methods. Moreover, with the help of degradation-aware context
control, our UniProcessor first shows the ability to individually handle a
single distortion in an image with multiple degradations.
