This paper reports an all-in-one image restoration model called InstructIR. The model is able to effectively repair various types and degrees of image degradation problems by leveraging degradation-specific information to guide the restoration process. Compared with previous image restoration methods, InstructIR has achieved significant improvements in image quality, with a performance improvement of +1dB. It is worth noting that although InstructIR is mainly trained using synthetic data, it also performs well when processing real-world haze and low-light images.
Reports indicate that the all-in-one image restoration model InstructIR uses degradation-specific information to guide the restoration model to effectively restore images from various types and levels of degradation. InstructIR improves +1dB compared to previous methods and uses synthetic data for training . Works surprisingly well on real-world foggy and low-light images.
The success of the InstructIR model lies in its effective utilization of degradation information and its good generalization ability to real scenes under training with synthetic data. This technology has important application prospects in the field of image processing and provides new solutions for improving image quality. In the future, this model is expected to be applied in more fields to further improve image quality and enhance user experience.