Abstract:

For most defogging algorithms, the effect on synthetic foggy images is different with the effect on real foggy images. Focusing on this problem, we propose a domain?adaptive single?image defogging algorithm which is based on a bidirectional conversion network. The bidirectional conversion network is designed to convert the foggy im?ages between the two domains. Our algorithm can be divided into two steps. Firstly, we reduce the difference be?tween the synthetic domain and the real domain by using the bidirectional conversion network; secondly, we remove fog from the input foggy images by using a convolutional neural network. To improve the effect and generalization ability of our algorithm, we use the synthetic RESIDE dataset and real foggy images as training data. Compared with results of some existing algorithms, the results show that our algorithm has better effects on foggy images of both domains, and our algorithm also improves the peak signal?to?noise ratio (PSNR) and structural similarity (SSIM).

Key words: Bidirectional conversion network, Image defogging, Convolutional neural network, Peak signal?to?noise ratio, Structural similarity

摘要：

当前图像去雾算法对人工合成图像域去雾效果与真实图像域去雾效果存在较大差异。针对该问题，提出了一种基于双向域转换网络的自适应单幅图像去雾算法。首先，构建双向域转换网络，实现人工合成有雾图像与真实有雾图像之间的自适应域转换；然后，通过卷积神经网络进行图像去雾。在实验中采用RESIDE人工合成的数据集以及真实环境的有雾图像作为训练集。结果表明，在人工合成图像域和真实图像域所提算法都有较好的处理能力和模型泛化能力，峰值信噪比(PSNR)、结构相似度(SSIM)等指标均有提高。

关键词: 双向域转换网络, 图像去雾, 卷积神经网络, 峰值信噪比, 结构相似度