Development of Efficient Techniques for Fog Removal from Digital Images

Abstract

With the increase in industrial production and human activities, the concentration of atmospheric particulate matter is substantial increased, leading to occurrence of fog and haze phenomenon. Due to these phenomenon, the visibility of scene gets reduced which is a major problem for many computer vision based applications. Hence, the scenes captured by computer vision systems called as images may suffer from poor visibility and low contrast. These make detection, tracking and recognition of objects within the images more difficult. Therefore visibility, contrast and features enhancement of images and videos captured in such a weather is an inevitable process called as fog removal or de-fogging process. In the past decade, many de-fogging techniques have emerged out of which the model based single image de-fogging techniques are visually appealing and produce qualitatively good results. One of the well known model based de-fogging technique is Dark Channel Prior (DCP). Although, it works well on various image types but it has some limitations including longer execution time, non uniform illumination and dullness in de-fogged images. Since DCP may fail for non sky areas in the image, the next de-fogging technique under consideration is Color Attenuation Prior (CAP). DCP works on RGB model whereas CAP works on HSV . CAP uses a linear model for depth map estimation and learns the parameters of this model with a supervised learning method. Although, CAP technique performs well on different type of foggy images but it has some limitations too. CAP uses Guided filter for refining initial depth map which is a well known smoothing filter but it may not work well for fine edge details. Also, the images obtained by CAP technique suffer from dullness and higher illumination variations due to consideration of homogeneous environment and a constant value of atmospheric light.