Images acquired under the influence of bad weather conditions such as haze, fog and other aerosols are deteriorated due to the dispersal of atmospheric particles which lead to color fading and contrast reduction, making it challenging for human interpretation and object feature recognition. Several methods for image haze removal have been introduced over the recent years, which consist of approaches used to extrapolate information such as contrast, scene depth, color channels and so on. In this

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... paper, we present a concise review of the current image dehazing methods. A comprehensive assessment and development on existing methods and related techniques is conducted based on their individual characteristics and principles. Qualitative and quantitative experimental evaluation of the state-of-the-art methods are conducted and discussed in depth. The paper further puts forward an overview of future trends within the research area. Key words: Image dehazing, image enhancement, physical models, quality assessment. (1) From (1) I represent the observed intensity of the haze image, J indicates the scene radiance, A indicates the global atmospheric light (Airlight) whiles t indicates the transmission medium which identifies the rest of pixels which are not distorted and attains the camera. Here the main aim of dehazing is to recover J from I and A. In this case, it is very essential and needful for computer vision systems and applications such as driver assistance systems [1], [2], outdoor video surveillance [3], [4], video assisted transportation [5], [6], remote future works must be directed to the adaptation of single image processing under different fields and also fast image segmentation algorithms in order to overcome challenging task of robustness, efficiency and comprehensiveness in the real-time domain.