Harnessing Spatial-Frequency Information for Enhanced Image Restoration

Image restoration aims to recover high-quality, clear images from those that have suffered visibility loss due to various types of degradation. Numerous deep learning-based approaches for image restoration have shown substantial improvements. However, there are two notable limitations: (a) Despite substantial spectral mismatches in the frequency domain between clean and degraded images, only a few approaches leverage information from the frequency domain. (b) Variants of attention mechanisms have been proposed for high-resolution images in low-level vision tasks, but these methods still require inherently high computational costs. To address these issues, we propose a Frequency-Aware Network (FreANet) for image restoration, which consists of two simple yet effective modules. We utilize a multi-branch/domain module that integrates latent features from the frequency and spatial domains using the discrete Fourier transform (DFT) and complex convolutional neural networks. Furthermore, we introduce a multi-scale pooling attention mechanism that employs average pooling along the row and column axes. We conducted extensive experiments on image restoration tasks, including defocus deblurring, motion deblurring, dehazing, and low-light enhancement. The proposed FreANet demonstrates remarkable results compared to previous approaches to these tasks.