HAIR: Hypernetworks-based All-in-One Image Restoration

Image restoration aims to recover a high-quality clean image from its degraded version. Recent progress in image restoration has demonstrated the effectiveness of All-in-One image restoration models in addressing various degradations simultaneously. However, these existing methods typically utilize the same parameters to tackle images with different degradation types, thus forcing the model to balance the performance between different tasks and limiting its performance on each task. To alleviate this issue, we propose HAIR, a \textbf{H}ypernetworks-based \textbf{A}ll-in-One \textbf{I}mage \textbf{R}estoration method that dynamically generates parameters based on input images. Specifically, HAIR consists of two main components, i.e., Classifier and Hyper Selecting Net (HSN). The Classifier is a simple image classification network used to generate a Global Information Vector (GIV) that contains the degradation information of the input image, and the HSN is a simple fully-connected neural network that receives the GIV and outputs parameters for the corresponding modules. Extensive experiments demonstrate that HAIR can significantly improve the performance of existing image restoration models in a plug-and-play manner, both in single-task and all-in-one settings. Notably, our innovative model, Res-HAIR, which integrates HAIR into the well-known Restormer, can obtain superior or comparable performance compared with current state-of-the-art methods. Moreover, we theoretically demonstrate that our proposed HAIR requires fewer parameters in contrast to the prevalent All-in-One methodologies. The code is available at \textcolor{blue}{\href{https://github.com/toummHus/HAIR}{https://github.com/toummHus/HAIR}.}