T-Net: Deep Stacked Scale-Iteration Network for Image Dehazing

Lirong Zheng, Yanshan Li, Kaihao Zhang, Wenhan Luo

Abstract: Haze reduces the visibility of image content and leads to failure in handling subsequent computer vision tasks. In this paper, we address the problem of single image dehazing by proposing a dehazing network named T-Net, which consists of a backbone network based on the U-Net architecture and a dual attention module. Multi-scale feature fusion can be achieved by using skip connections with a new fusion strategy. Furthermore, by repeatedly unfolding the plain T-Net, Stack T-Net is proposed to take advantage of the dependence of deep features across stages via a recursive strategy. To reduce network parameters, the intra-stage recursive computation of ResNet is adopted in our Stack T-Net. We take both the stage-wise result and the original hazy image as input to each T-Net and finally output the prediction of the clean image. Experimental results on both synthetic and real-world images demonstrate that our plain T-Net and the advanced Stack T-Net perform favorably against state-of-the-art dehazing algorithms and show that our Stack T-Net could further improve the dehazing effect, demonstrating the effectiveness of the recursive strategy.