SCGA-Net: Skip Connections Global Attention Network for Image Restoration

Abstract

Deep convolutional neural networks (DCNN) have shown their advantages in the image restoration tasks. But most existing DCNN-based methods still suffer from the residual corruptions and coarse textures. In this paper, we propose a general framework ''Skip Connections Global Attention Network'' to focus on the semantics delivery from shallow layers to deep layers for low-level vision tasks including image dehazing, image denoising, and low-light image enhancement. First of all, by applying dense dilated convolution and multi-scale feature fusion mechanism, we establish a novel encoder-decoder network framework to aggregate large-scale spatial context and enhance feature reuse. Secondly, the solution we proposed for skipping connection uses attention mechanism to constraint information, thereby enhancing the high-frequency details of feature maps and suppressing the output of corruptions. Finally, we also present a novel attention module dubbed global constraint attention, which could effectively captures the relationship between pixels on the entire feature maps, to obtain the subtle differences among pixels and produce an overall optimal 3D attention maps. Extensive experiments demonstrate that the proposed method achieves significant improvements over the state-of-the-art methods in image dehazing, image denoising, and low-light image enhancement.