Computational Vision in Bad Weather

计算视觉在从图像恢复场景属性的传感器和算法的开发方面取得了重大进展。然而，几乎所有的视觉工作都是基于这样的假设，即观察者沉浸在透明的介质（空气）中，而感兴趣的物体是不透明的。假设被物体反射的光线在没有衰减或改变的情况下传播到观察者。因此，现有的视觉传感器和算法仅用于应对晴朗的天气。然而，在实践中，视觉系统必须考虑到通常被称为恶劣天气的整个大气条件。它必须在各种条件下继续发挥作用，包括霾、雾、雨、冰雹和雪。该提案概述了一项全面的研究计划，旨在开发在恶劣天气下有助于视力的模型和方法。 第一步是了解不同类型天气状况的视觉表现。 为此，我们将利用已经知道的关于大气光学的知识。 由于大气调制了从景物点传递到观察者的信息，因此它可以被看作是视觉信息编码的一种机制。我们建议开发一个通用的计算框架，利用亮度和颜色的变化，引起的恶劣天气。 基于这个框架，我们将开发模型和方法，用于从不同（未知）天气条件下拍摄的图像中恢复相关的场景属性，例如真实（晴朗天气）颜色和三维结构。 这些模型和方法在场景理解、自主导航和视频监控等领域有着重要的应用价值。 此外，我们希望创建一个广泛的图像/视频数据库，以捕捉天气引起的各种视觉效果。我们相信，这样的数据库将使研究人员更容易研究计算视觉的这一重要领域。

Computational vision has made significant strides in the development of sensors and algorithms that recover scene properties from images. However, virtually all work in vision is based on the assumption that the observer is immersed in a transparent medium (air)and that the objects of interest are opaque. It is assumed that light rays reflected by the objects travel to the observer without attenuation or alteration. Therefore, existing vision sensors and algorithms have been created only to deal with clear weather. In practice, however, a vision system must reckon with the entire spectrum of atmospheric conditions commonly known as bad weather. It must continue to perform in the presence of a variety of conditions,including, haze, fog, rain, hail and snow.This proposal outlines a comprehensive research program geared towards the development of models and methods that can aid vision in bad weather. The first step is to understand the visual manifestations of different types of weather conditions. For this, we will draw on what is already known about the optics of the atmosphere. Since the atmosphere modulates the information carried from a scene point to the observer, it can be viewed as a mechanism of visual information coding. We propose the development of a general computational framework that exploits the brightness and color changes that are induced by bad weather. Based on this framework, we will develop models and methods for recovering pertinent scene properties such as true (clear weather) color and three-dimensional structure, from images taken under different (unknown) weather conditions. Such models and methods have obvious applications in scene understanding, autonomous navigation and video surveillance. In addition, we wish to create an extensive image/video database that captures the wide range of visual effects caused by weather. We believe that such a database will make it easier for researchers to study this important area of computational vision.