Multi-Scale Density-Aware Network for Single Image Dehazing
Tao Gao, Yao Liu, Peng Cheng, Ting Chen, Lidong Liu
Abstract
Dehazing based on deep learning has attracted a lot of attention recently. Most dehazing networks seldom consider two critical features of real outdoor-scene haze, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.</i> , depth and haze density, resulting in degraded performance on real hazy images compared with synthetic hazy images. Moreover, the uncertainty problem is crucial in the image restoration field, but it is often ignored. In this letter, we propose a novel multi-scale density-aware network (MSDAN) for single image dehazing, where a key dual feedback module (DFB) is proposed and embedded in the decoder part of MSDAN. Furthermore, the DFB includes a feedforward mechanism and two feedback mechanisms: feature feedback (FF) and transmission feedback (TF). Specifically, the feedforward mechanism predicts a low-scale transmission map ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$t$</tex-math></inline-formula> -map), while FF and TF aim to enhance confident features to reduce model uncertainty in the training process and correct features by introducing depth and density information. In addition, two novel modules: confident feature attention module (CFA) and transmission adjustment module (TADJ) are proposed as cores for confident features estimation of FF and TF, respectively. Extensive quantitative and qualitative experiments are conducted on several public datasets, which demonstrate that the proposed algorithm outperforms the state-of-the-art algorithms.