Image dehazing has been a popular topic of research for a long time. Previous
deep learning-based image dehazing methods have failed to achieve satisfactory
dehazing effects on both synthetic datasets and real-world datasets, exhibiting
poor generalization. Moreover, single-stage networks often result in many
regions with artifacts and color distortion in output images. To address these
issues, this paper proposes a two-stage image dehazing network called TSNet,
mainly consisting of the multi-scale fusion module (MSFM) and the adaptive
learning module (ALM). Specifically, MSFM and ALM enhance the generalization of
TSNet. The MSFM can obtain large receptive fields at multiple scales and
integrate features at different frequencies to reduce the differences between
inputs and learning objectives. The ALM can actively learn of regions of
interest in images and restore texture details more effectively. Additionally,
TSNet is designed as a two-stage network, where the first-stage network
performs image dehazing, and the second-stage network is employed to improve
issues such as artifacts and color distortion present in the results of the
first-stage network. We also change the learning objective from ground truth
images to opposite fog maps, which improves the learning efficiency of TSNet.
Extensive experiments demonstrate that TSNet exhibits superior dehazing
performance on both synthetic and real-world datasets compared to previous
state-of-the-art methods.

该研究提出了一种名为 TSNet 的两阶段图像去雾网络，主要由多尺度融合模块（MSFM）和自适应学习模块（ALM）组成，通过增强泛化性能和恢复纹理细节来实现更好的去雾效果。实验证明，相较于之前的方法，TSNet 在合成和真实数据集上表现出卓越的性能。