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

TL;DR

HAIR introduces a hypernetwork-based approach that dynamically generates task-specific parameters for image restoration, significantly improving performance and efficiency over existing methods in handling diverse degradations.

Contribution

The paper proposes HAIR, a novel hypernetwork framework that adapts image restoration parameters based on input degradation, enhancing flexibility and performance in All-in-One models.

Findings

HAIR improves restoration performance in single-task and All-in-One settings.

Res-HAIR achieves state-of-the-art or comparable results to existing methods.

Theoretically, HAIR requires fewer parameters than embedding-based approaches.

Abstract

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 unknown degradations simultaneously. However, these existing methods typically utilize the same parameters to tackle images with different types of degradation, 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 Hypernetworks-based All-in-One Image Restoration plug-and-play method that generates parameters based on the input image and thus makes the model to adapt to specific degradation dynamically. 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 proposed 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 to achieve a given small enough error, our proposed HAIR requires fewer parameters in contrast to mainstream embedding-based All-in-One methods. The code is available at https://github.com/toummHus/HAIR.