Adversarial learning-based image defogging methods have been extensively studied in computer vision due to their remarkable performance. However, most existing methods have limited defogging capabilities for real cases because they are trained on the paired clear and synthesized foggy images of the same scenes. In addition, they have limitations in preserving vivid color and rich textual details in defogging. To address these issues, we develop a novel generative adversarial network, called quad-path cycle consistent adversarial network (QPC-Net), for single image defogging. QPC-Net consists of a Fog2Fogfree block and a Fogfree2Fog block. In each block, there are three learning-based modules, namely, fog removal, color-texture recovery, and fog synthetic, which sequentially compose dual-path that constrain each other to generate high quality images. Specifically, the color-texture recovery model is designed to exploit the self-similarity of texture and structure information by learning the holistic channel-spatial feature correlations between the foggy image with its several derived images. Moreover, in the fog synthetic module, we utilize the atmospheric scattering model to guide it to improve the generative quality by focusing on an atmospheric light optimization with a novel sky segmentation network. Extensive experiments on both synthetic and real-world datasets show that QPC-Net outperforms state-of-the-art defogging methods in terms of quantitative accuracy and subjective visual quality.

翻译：基于对抗学习的图像去雾方法因其卓越性能在计算机视觉领域得到了广泛研究。然而，现有方法大多在相同场景的配对清晰图像与合成有雾图像上训练，导致实际案例中的去雾能力有限。此外，它们在去雾过程中难以保持鲜艳色彩和丰富纹理细节。为解决这些问题，我们提出了一种新型生成对抗网络——四路径循环一致性对抗网络（QPC-Net），用于单图像去雾。QPC-Net包含一个Fog2Fogfree模块和一个Fogfree2Fog模块。每个模块中设有三个基于学习的子模块（去雾、色彩纹理恢复、雾合成），它们依次构成相互约束的双路径结构以生成高质量图像。具体而言，色彩纹理恢复模型通过学习有雾图像及其若干衍生图像间的全局通道-空间特征相关性，利用纹理与结构信息的自相似性。此外，在雾合成模块中，我们引入大气散射模型进行引导，通过新型天空分割网络聚焦于大气光优化，从而提升生成质量。在合成数据集与现实世界数据集上的大量实验表明，QPC-Net在定量精度和主观视觉质量上均优于当前最先进的去雾方法。