Vision Transformer (ViT) has shown impressive performance in image restoration due to its ability to capture a large receptive field. However, its complexity grows quadratically with input resolution, limiting its applicability for high-resolution images. In contrast, Convolutional Neural Networks (CNNs) are computationally efficient but are constrained by their inherently local receptive fields, which limit their ability to capture long-range pixel relationships. To address these challenges, we propose StarIR, which possesses the efficiency of CNNs while also capturing a large receptive field, similar to Transformers. StarIR incorporates two key innovations: 1) a dual-domain representation learning framework, with one branch processing spatial details and the other focusing on mesoscale interactions in the frequency domain; and 2) a high-dimensional feature fusion mechanism, the Star operation, which fuses information from both domains through element-wise multiplication, thereby enhancing representational capacity without increasing network width and depth. Our Star operation is followed by a channel attention unit to facilitate global feature modeling and enhance channel-wise interactions. Building on our straightforward yet powerful design principles, StarIR achieves state-of-the-art performance across 21 datasets covering six single-degradation image restoration tasks. Furthermore, our model performs favorably against leading algorithms in two all-in-one settings and demonstrates robustness on two composite-degradation datasets. In addition, StarIR extends well to several domain-specific applications, including ultra-high-definition (UHD) imaging, remote sensing, medical imaging, and underwater image enhancement.
Cui et al. (2026) studied this question.