Haze Removal Using Color Attenuation Prior With Varying Scattering Coefficients

Outdoor images taken in bad weather (e.g., foggy or hazy) usually lose contrast and color fidelity, resulting from the fact that light is absorbed and scattered by the turbid medium such as particles and water droplets in the atmosphere during the process of propagation. Moreover, most automatic systems, which strongly depend on the definition of the input images, fail to work normally caused by the degraded images. Therefore, improving the technique of image haze removal will benefit many image understanding and computer vision applications such as aerial imagery, image classification, image/video retrieval, remote sensing and video analysis and recognition. Since concentration of the haze is different from place to place and it is hard to detect in a hazy image, image dehazing is thus a challenging task.

In almost every practical scenario the light reflected from a surface is scattered in the atmosphere before it reaches the camera. This is due to the presence of aerosols such as dust, mist, and fumes which deflect light from its original course of propagation. In long distance photography or foggy scenes, this process has a substantial effect on the image in which contrasts are reduced and surface colors become faint. Such degraded photographs often lack visual vividness and appeal, and moreover, they offer a poor visibility of the scene contents. This effect may be an annoyance to amateur, commercial, and artistic photographers as well as undermine the quality of underwater and aerial photography. This may also be the case for satellite imaging which is used for many purposes including cartography and web mapping, land-use planning, archeology, and environmental studies.

Early researchers use the traditional techniques of image processing to remove the haze from a single image (for instance, histogram-based dehazing methods ([4], [5]). However, the dehazing effect is limited, because a single hazy image can hardly provide much information. Later, researchers try to improve the dehazing performance with multiple images. In [4] and [7], polarization based methods are used for dehazing with multiple images which are taken with different degrees of polarization. It is based on the fact that usually airlight scattered by atmospheric particles is partially polarized. Polarization filtering alone cannot remove the haze effects, except in restricted situations. Our method, however, works under a wide range of atmospheric and viewing conditions. We analyze the image formation process, taking into account polarization effects of atmospheric scattering.

In Chromatic Framework method [8], propose haze removal approaches with multiple images of the same scene under different weather conditions. In this develop a general chromatic framework for the analysis of images taken under poor weather conditions. The wide spectrum of atmospheric particles makes a general study of vision in bad weather hard. So, we limit ourselves to weather conditions that result from fog and haze. We begin by describing the key mechanisms of scattering. Next, we analyze the dichromatic model proposed, and experimentally verify it for fog and haze. Then, we derive several useful geometric constraints on scene color changes due to different but unknown atmospheric conditions.