A Review on Haze Removal Methods in Image and Video for Object Detection and Tracking

Abstract

This chapter includes the literature work of several haze and fog removal methods for object detection and tracking. Object detection and tracking is an active area of the computer vision and image processing. Image processing is the field which is used to process the images to extract data and information from the processed images. An image consists of a single frame which captures a single and static instance of a naturally occurring event, whereas a video contains many instances of static images of a naturally occurring event displayed in one second. So we can say that a single static image in a video is called a video frame. Various object detection methods are available, but basic background subtraction method is widely used to detect the object from the image and video. Background subtraction method computes difference between current frame and background model in order to extract information for many computer vision applications. Dehazing or removal of haze of any image and video for object detection is an important research work nowadays because due to environmental conditions like haze, fog, dust and mist, etc., the image and video quality is deteriorated. While taking photograph and taking video, clarity and visibility automatically decreases if distance increases between the camera and the desired object. Generally, the haze is formed by air light and attenuation in the atmospheric environment. Air light increases the whiteness in the environment and attenuation decreases and reduces the contrast which results in reducing visibility of the image and the video. The suspended particles present in the atmosphere result in scattering of light, and it destroys the quality of the image. Haze results in mixing of the reflected light from the image with the additional light present in atmosphere. Reducing visibility of any image and video due to haze, it affects the various image processing and computer vision-based applications such as surveillance, object detection, tracking, etc. Haze removal methods are very beneficial in traffic surveillance system to avoid the accidents on the highways in the hazy and the dusty weather to track the vehicles. Haze removal techniques are also applicable in agriculture to detect the unwanted elements and objects in the agriculture field to avoid the crops damage in foggy weather. The most important application of object detection is in border security. In the night, vision is not clear with the dusty atmospheric conditions, or we can say in the presence of haze, vision is blurred. So, for the security at borders, haze removal algorithms must work for the security persons to get the clear vision. To control the air traffic in the presence of haze, these algorithms must be used for object detection. In this chapter, various methods for haze removal such as DCP (Dark Channel Prior), polarization filter, CLAHA (contrast limited adaptive histogram equalization), Mix-CLAHA, etc. are to be discussed which are used for improving the quality of the image and the video and help in various image processing and computer vision-based applications. These haze removal methods and defogging techniques are used for single image and multiple images. Haze removal techniques are used to find the clarity in the images in the presence of haze particles. This chapter includes literature work, challenges and benefits and future work on haze removal methods.