论文标题
评估逼真的阳光雾气下的单图像飞机
Evaluating Single Image Dehazing Methods Under Realistic Sunlight Haze
论文作者
论文摘要
阴霾会大大降低可见性和图像质量，从而降低计算机视觉任务（例如对象检测）的性能。尽管经过广泛的研究，但单图像除尘是一个具有挑战性且不足的问题。大多数现有方法都认为，雾度具有均匀/均匀的分布，而雾霾可以具有单一颜色，即类似于烟雾的灰色白色，而实际上，雾霾可以以不同的图案和颜色的形式分布不均匀。在本文中，我们专注于阳光所产生的雾霾，因为它是野外最普遍的雾霾之一。阳光可以由于阳光射线而引起的急剧变化以及由于白天的阳光颜色变化而引起的雾化不均匀的密度变化，并产生不均匀的雾度。这给图像飞去方法带来了新的挑战。为了使这些方法是实用的，需要解决此问题。为了量化挑战并评估这些方法的性能，我们提出了一个阳光雾霾基准数据集（Haze），其中包含107个朦胧的图像，这些图像具有不同类型的雾霾，这些雾霾由阳光产生，具有多种强度和颜色。我们根据PSNR，SSIM，CIEDE2000，PI和NIQE评估了该基准数据集上一组代表性的最先进的图像脱掩护方法。这揭示了当前方法的局限性，并质疑其基本假设及其实用性。
Haze can degrade the visibility and the image quality drastically, thus degrading the performance of computer vision tasks such as object detection. Single image dehazing is a challenging and ill-posed problem, despite being widely studied. Most existing methods assume that haze has a uniform/homogeneous distribution and haze can have a single color, i.e. grayish white color similar to smoke, while in reality haze can be distributed non-uniformly with different patterns and colors. In this paper, we focus on haze created by sunlight as it is one of the most prevalent type of haze in the wild. Sunlight can generate non-uniformly distributed haze with drastic density changes due to sun rays and also a spectrum of haze color due to sunlight color changes during the day. This presents a new challenge to image dehazing methods. For these methods to be practical, this problem needs to be addressed. To quantify the challenges and assess the performance of these methods, we present a sunlight haze benchmark dataset, Sun-Haze, containing 107 hazy images with different types of haze created by sunlight having a variety of intensity and color. We evaluate a representative set of state-of-the-art image dehazing methods on this benchmark dataset in terms of standard metrics such as PSNR, SSIM, CIEDE2000, PI and NIQE. This uncovers the limitation of the current methods, and questions their underlying assumptions as well as their practicality.
