Mix-order Attention Networks for Image Restoration

Convolutional neural networks (CNNs) have obtained great success in image restoration tasks, like single image denoising, demosaicing, and super-resolution. However, most existing CNN-based methods neglect the diversity of image contents and degradations in the corrupted images and treat channel-wise features equally, thus hindering the representation ability of CNNs. To address this issue, we propose a deep mix-order attention networks (MAN) to extract features that capture rich feature statistics within networks. Our MAN is mainly built on simple residual blocks and our mix-order channel attention (MOCA) module, which further consists of feature gating and feature pooling blocks to capture different types of semantic information. With our MOCA, our MAN can be flexible to handle various types of image contents and degradations. Besides, our MAN can be generalized to different image restoration tasks, like image denoising, super-resolution, and demosaicing. Extensive experiments demonstrate that our method obtains favorably against state-of-the-art methods in terms of quantitative and qualitative metrics.