Dual-domain strip attention for image restoration

Image restoration aims to reconstruct a latent high-quality image from a degraded observation. Recently, the usage of Transformer has significantly advanced the state-of-the-art performance of various image restoration tasks due to its powerful ability to model long-range dependencies. However, the quadratic complexity of self-attention hinders practical applications. Moreover, sufficiently leveraging the huge spectral disparity between clean and degraded image pairs can also be conducive to image restoration. In this paper, we develop a dual-domain strip attention mechanism for image restoration by enhancing representation learning, which consists of spatial and frequency strip attention units. Specifically, the spatial strip attention unit harvests the contextual information for each pixel from its adjacent locations in the same row or column under the guidance of the learned weights via a simple convolutional branch. In addition, the frequency strip attention unit refines features in the spectral domain via frequency separation and modulation, which is implemented by simple pooling techniques. Furthermore, we apply different strip sizes for enhancing multi-scale learning, which is beneficial for handling degradations of various sizes. By employing the dual-domain attention units in different directions, each pixel can implicitly perceive information from an expanded region. Taken together, the proposed dual-domain strip attention network (DSANet) achieves state-of-the-art performance on 12 different datasets for four image restoration tasks, including image dehazing, image desnowing, image denoising, and image defocus deblurring.