Fog removal from an image is an active research topic in computer vision. However, current literature is weak in the following two areas which in many ways are hindering progress for developing defogging algorithms. First, there is no true real-world and naturally occurring foggy image datasets suitable for developing defogging models. Second, there is no suitable mathematically simple and easy to use image quality assessment (IQA) methods for evaluating the visual quality of defogged images. We address these two aspects in this paper. We first introduce a new foggy image dataset called multiple real-world foggy image dataset (MRFID). MRFID contains foggy and clear images of 200 outdoor scenes. For each scene, one clear image and 4 foggy images of different densities defined as slightly foggy, moderately foggy, highly foggy, and extremely foggy, are manually selected from images taken from these scenes over the course of one calendar year. We then process the foggy images of MRFID using 16 defogging methods to obtain 12,800 defogged images (DFIs) and perform a comprehensive subjective evaluation of the visual quality of the DFIs. Through collecting the mean opinion score (MOS) of 120 subjects and evaluating a variety of fog-relevant image features, we have developed a new Fog-relevant Feature based SIMilarity index (FRFSIM) for assessing the visual quality of DFIs. We present extensive experimental results to show that our new visual quality assessment measure, the FRFSIM, is more consistent with the MOS than other IQA methods and is therefore more suitable for evaluating defogged images than other state-of-the-art IQA methods. Our dataset and relevant code are available at http://www.vistalab.ac.cn/MRFID-for-defogging/ .

中文翻译：

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图像除雾质量评估：实际数据库和方法

从图像中去除雾气是计算机视觉中一个活跃的研究主题。然而，当前的文献在以下两个方面是薄弱的，这在许多方面阻碍了开发除雾算法的进展。首先，没有适合开发除雾模型的真实世界和自然发生的模糊图像数据集。其次，没有合适的数学上简单易用的图像质量评估（IQA）方法来评估经过除雾的图像的视觉质量。我们在本文中解决了这两个方面。我们首先介绍一个新的模糊图像数据集，称为多真实世界模糊图像数据集（MRFID）。MRFID包含200个室外场景的模糊且清晰的图像。对于每个场景，一个不同密度的清晰图像和4个有雾图像定义为轻度有雾，中度有雾，高度有雾和极度有雾，从一个日历年的过程中从这些场景拍摄的图像中手动选择。然后，我们使用16种除雾方法处理MRFID的模糊图像，以获得12,800张除雾图像（DFI），并对DFI的视觉质量进行全面的主观评估。通过收集120个对象的平均意见评分（MOS）并评估各种与雾相关的图像特征，我们开发了一种基于雾相关特征的新相似性指数（FRFSIM），用于评估DFI的视觉质量。我们提供了广泛的实验结果，表明我们的新视觉质量评估方法FRFSIM与MOS的一致性比其他IQA方法更高，因此比其他最新的IQA方法更适合评估雾化图像。我们的数据集和相关代码位于http://www.vistalab.ac.cn/MRFID-for-defogging/ 。