ARF: Arbitrary Routing Framework for All-in-One Image Restoration

All-in-one image restoration methods, as opposed to conventional image restoration methods, reconstruct images impaired by various degradations within a unified model, eliminating the need for separate network parameters for each task. However, current all-in-one image restoration approaches tackle various types of image degradation using an identical underlying model, neglecting the inherent variability in complexity across different image restoration tasks, resulting in inefficient allocation of computational resources. To address this limitation, this article introduces the arbitrary routing framework (ARF), designed to effectively assess the difficulty of image restoration tasks and identify the most suitable network structure based on these complexities. This framework can be integrated with existing all-in-one image restoration models, enabling efficient inference by activating various proportions of the entire network, that is subnetworks, based on their task-specific complexities. More specifically, the ARF comprises two principal components: 1) the arbitrary routing backbone (ARB) and 2) a task-specific neural architecture search (T-NAS). The ARB incorporates a routing layer between consecutive convolutional groups, offering a wide array of potential subnetwork configurations while adding only negligible extra parameters. Concurrently, T-NAS autonomously identifies the most effective subnetworks for each image restoration task, optimizing both performance and efficiency through an efficiency-aware reward function. Comprehensive experiments across various image restoration tasks demonstrate that the ARF significantly improves performance metrics, that is, an increase of 0.31 in reconstruction PSNR, while also achieving a notable reduction in computational demands by 37.1% compared with the benchmark AirNet method. The code has been made available in the supplementary materials.