Domain Adaptation for Image Dehazing

By Yuanjie Shao, Lerenhan Li, Wenqi Ren, Changxin Gao, Nong Sang. Computer Vision and Pattern Recognition, 2020

401 citations recorded by the source corpus.

Image dehazing using learning-based methods has achieved state-of-the-art performance in recent years. However, most existing methods train a dehazing model on synthetic hazy images, which are less able to generalize well to real hazy images due to domain shift. To address this issue, we propose a domain adaptation paradigm, which consists of an image translation module and two image dehazing modules. Specifically, we first apply a bidirectional translation network to bridge the gap between the synthetic and real domains by translating images from one domain to another. And then, we use images before and after translation to train the proposed two image dehazing networks with a consistency constraint. In this phase, we incorporate the real hazy image into the dehazing training via exploiting the properties of the clear image (e.g., dark channel prior and image gradient smoothing) to further improve the domain adaptivity. By training image translation and dehazing network in an end-to-end manner, we can obtain better effects of both image translation and dehazing. Experimental results on both synthetic and real-world images demonstrate that our model performs favorably against the state-of-the-art dehazing algorithms.

Subfields: Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/7c7b46ac41f12074e5cdd7187efbaa33f8eab6b4

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Chongyi Li
2. Chunle Guo
3. Runmin Cong
4. Wenqi Ren
5. Saeed Anwar
6. Yanyun Qu
7. Yuan Xie
8. Yuda Song

In Media Technology

1. Runmin Cong
2. Chunle Guo
3. Chongyi Li
4. Yanyun Qu
5. Yuan Xie
6. Wenqi Ren
7. Saeed Anwar
8. Yuda Song

Citations in the corpus

• Image Dehazing Transformer with Transmission-Aware 3D Position Embedding (2022, methodology): “Quantitative comparisons with two additional stateof-the-art methods (CL [10] and DA [9]) on SOTS, DenseHaze, and NH-Haze datasets.”
• PSD: Principled Synthetic-to-Real Dehazing Guided by Physical Priors (2021, background): “12–18, PSD produces significant improvements on backbone models and performs well against the state-of-the-art domain adaptation dehazing [13].”
• Contrastive Learning for Compact Single Image Dehazing (2021, methodology): “Previous works [35, 30, 34, 23, 10, 42] usually employ the fixed grid kernel (e.”
• Vision Transformers for Single Image Dehazing (2022, methodology): “In recent years, deep learning has made a big hit in computer vision, and researchers have proposed a large number of image dehazing methods based on deep convolutional neural networks (CNNs) [8], [9], [10], [11], [12], [13], [14], [15], [16], [17], [18], [19], [20], [21], [22].”
• ALL Snow Removed: Single Image Desnowing Algorithm Using Hierarchical Dual-tree Complex Wavelet Representation and Contradict Channel Loss (2021, background): “’wDH’ and ’woDH’ denote the cases with and without dehazing by the [34], respectively.”