Large Kernel Network for Efficient Image Restoration.

The pursuit of large receptive fields has shaped the evolution of computer vision frameworks, spanning from convolutional neural networks (CNNs) to Transformers and Mamba. Recently, large-kernel operations have revitalized CNNs, making them competitive once again and widely applicable across diverse vision tasks. However, scaling kernel sizes inevitably results in substantial growth in both parameters and computational overhead. Consequently, existing approaches are often limited to small kernels or resort to decoupled designs for large kernels. In this paper, we propose a simple and efficient large kernel network for image restoration, termed ArtIR, motivated by the channel redundancy observed in image restoration models. Specifically, ArtIR applies adaptive large-kernel operations to a collapsed single channel and employs an ultra-lightweight channel attention mechanism to restore channel diversity. To complement local features, we further introduce a large kernel fusion module that integrates multi-scale information. Unlike most prior methods that focus on a narrow set of restoration tasks, we comprehensively evaluate ArtIR across single-degradation, all-in-one, and composite degradation scenarios. Beyond generic restoration, we also assess our model on domain-specific applications such as ultra-high-definition restoration, medical imaging, and remote sensing. Extensive experiments demonstrate that ArtIR achieves state-of-the-art performance while maintaining high efficiency and fast inference.