During recent years, we have witnessed a rapid development of wireless network technologies which have revolutionized the way people take and share multimedia content. However, images captured in the outdoor scenes usually suffer from limited visibility due to suspended atmospheric particles, which directly affects the quality of photos. Despite the recent progress of image dehazing methods, the visual quality of dehazed results still needs further improvement. In this paper, we propose a deep convolutional neural network (CNN) for single image dehazing called PDR-Net, which includes a perception-inspired haze removal subnetwork that reconstructs the latent dehazed image and a refinement subnetwork that further enhances the contrast and color properties of the dehazed result by joint multi-term loss optimization. Compared to the previous methods, our method combines the advantages of existing indoor and outdoor image dehazing training data, which makes the proposed PDR-Net generalized to various hazy images and effective for improving the visual quality of the dehazed results. Extensive experiments demonstrate that the proposed method achieves comparable and even better performance on both real and synthetic images in qualitative and quantitative metrics. Additionally, the potential usage of our method in high-level vision tasks is discussed.

中文翻译：

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PDR-Net：具有细化的感知启发单图像去雾网络

近年来，我们见证了无线网络技术的快速发展，这些技术彻底改变了人们获取和共享多媒体内容的方式。然而，在室外场景中拍摄的图像通常会由于悬浮的大气颗粒而导致可见度有限，这直接影响了照片的质量。尽管最近图像去雾方法取得了进展，但去雾结果的视觉质量仍有待进一步提高。在本文中，我们提出了一种称为 PDR-Net 的用于单个图像去雾的深度卷积神经网络 (CNN)，它包括一个受感知启发的去雾子网络，用于重建潜在的去雾图像，以及一个进一步增强对比度和颜色特性的细化子网络联合多项损失优化的去雾结果。与之前的方法相比，我们的方法结合了现有室内和室外图像去雾训练数据的优点，这使得所提出的 PDR-Net 可以推广到各种有雾的图像，有效提高去雾结果的视觉质量。大量实验表明，所提出的方法在定性和定量指标上在真实和合成图像上均取得了可比甚至更好的性能。此外，还讨论了我们的方法在高级视觉任务中的潜在用途。大量实验表明，所提出的方法在定性和定量指标上在真实和合成图像上均取得了可比甚至更好的性能。此外，还讨论了我们的方法在高级视觉任务中的潜在用途。大量实验表明，所提出的方法在定性和定量指标上在真实和合成图像上均取得了可比甚至更好的性能。此外，还讨论了我们的方法在高级视觉任务中的潜在用途。