Single Image Haze Removal Using Dark Channel Prior
Single Image Haze Removal Using Dark Channel Prior
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DOI：
10.18535/ijecs/v5i1.12
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发表时间：
2016
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影响因子：
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通讯作者：
Mohinder Malhotra
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文献类型：
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作者：
Mohinder Malhotra
雾霾给许多计算机视觉/图形应用带来了麻烦。它降低了场景的能见度，降低了户外监控系统的可靠性；降低了卫星图像的清晰度；还改变了日常照片的颜色，降低了对比度，这对摄影师来说是一个恼人的问题。因此，去除图像中的雾霾是计算机视觉和计算机图形学领域中一个重要且被广泛要求的课题。主要的挑战在于这个问题的模糊性。雾气会衰减从场景中反射的灯光，并进一步将其与大气中的一些附加灯光混合。雾霾去除的目标是从混合光中恢复反射光(即场景颜色)。这个问题在数学上是模棱两可的：在给定混合光的情况下，有无限多的解。我们如何知道哪种解决方案是正确的？我们需要在消除阴霾的过程中回答这个问题。歧义是许多计算机视觉问题的共同挑战。在数学方面，模糊性是因为方程的数量小于未知数的数量。计算机视觉中解决歧义的方法大致可以分为两种策略。第一种是获取更多的已知变量，例如，一些雾霾去除算法在不同设置(如偏光镜)下捕获同一场景的多幅图像，但在实际应用中要获得额外的图像并不容易。第二种策略是使用一些知识或假设施加额外的约束。本文中的所有图像都最好在电子版中查看。这种方法更实用，因为它只需要一张图像。为此，本文重点研究了单幅图像的去雾方法。关键是要找到一个合适的人选。在许多计算机视觉主题中，前科都很重要。当事实不能直接获得时，先验信息告诉算法“我们可以预先知道关于该事实的什么”。通常，先验可以是一些统计/物理属性、规则或启发式假设。算法的性能通常由先验信息有效的程度来决定。计算机视觉中广泛使用的先验知识有光滑性先验、稀疏先验和对称性先验。在本文中，我们开发了一种有效但非常简单的先验算法，称为暗通道先验算法，用于去除单幅图像中的模糊。暗通道先验是室外无雾霾图像的统计特性：这些图像中的大多数斑块都应该包含至少一个颜色通道中暗的像素。这些暗像素可能是由于阴影、色彩、几何形状或其他因素造成的。该先验知识为每个像素提供了约束，从而解决了问题的模糊性。将这一点与物理雾霾成像模型相结合，我们可以轻松地恢复高质量的无雾霾图像。
Haze brings troubles to many computer vision/graphics applications. It reduces the visibility of the scenes and lowers the reliability of outdoor surveillance systems; it reduces the clarity of the satellite images; it also changes the colors and decreases the contrast of daily photos, which is an annoying problem to photographers. Therefore, removing haze from images is an important and widely demanded topic in computer vision and computer graphics areas. The main challenge lies in the ambiguity of the problem. Haze attenuates the light reflected from the scenes, and further blends it with some additive light in the atmosphere. The target of haze removal is to recover the reflected light (i.e., the scene colors) from the blended light. This problem is mathematically ambiguous: there are an infinite number of solutions given the blended light. How can we know which solution is true? We need to answer this question in haze removal. Ambiguity is a common challenge for many computer vision problems. In terms of mathematics, ambiguity is because the number of equations is smaller than the number of unknowns. The methods in computer vision to solve the ambiguity can roughly categorized into two strategies. The first one is to acquire more known variables, e.g., some haze removal algorithms capture multiple images of the same scene under different settings (like polarizers).But it is not easy to obtain extra images in practice. The second strategy is to impose extra constraints using some knowledge or assumptions .All the images in this thesis are best viewed in the electronic version. This way is more practical since it requires as few as only one image. To this end, we focus on single image haze removal in this thesis. The key is to find a suitable prior. Priors are important in many computer vision topics. A prior tells the algorithm "what can we know about the fact beforehand" when the fact is not directly available. In general, a prior can be some statistical/physical properties, rules, or heuristic assumptions. The performance of the algorithms is often determined by the extent to which the prior is valid. Some widely used priors in computer vision are the smoothness prior, sparsity prior, and symmetry prior. In this thesis, we develop an effective but very simple prior, called the dark channel prior, to remove haze from a single image. The dark channel prior is a statistical property of outdoor haze-free images: most patches in these images should contain pixels which are dark in at least one color channel. These dark pixels can be due to shadows, colorfulness, geometry, or other factors. This prior provides a constraint for each pixel, and thus solves the ambiguity of the problem. Combining this prior with a physical haze imaging model, we can easily recover high quality haze-free images.