A review on dark channel prior based image dehazing algorithms <s> Introduction <s> Current vision systems are designed to perform in clear weather. Needless to say, in any outdoor application, there is no escape from "bad" weather. Ultimately, computer vision systems must include mechanisms that enable them to function (even if somewhat less reliably) in the presence of haze, fog, rain, hail and snow. We begin by studying the visual manifestations of different weather conditions. For this, we draw on what is already known about atmospheric optics. Next, we identify effects caused by bad weather that can be turned to our advantage. 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. Based on this observation, we develop models and methods for recovering pertinent scene properties, such as three-dimensional structure, from images taken under poor weather conditions. <s> BIB001 </s> A review on dark channel prior based image dehazing algorithms <s> Introduction <s> We present an approach to easily remove the effects of haze from images. 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. We then invert the process to enable the removal of haze from images. The method can be used with as few as two images taken through a polarizer at different orientations. This method works instantly, without relying on changes of weather conditions. We present experimental results of complete dehazing in far from ideal conditions for polarization filtering. We obtain a great improvement of scene contrast and correction of color. As a by product, the method also yields a range (depth) map of the scene, and information about properties of the atmospheric particles. <s> BIB002 </s> A review on dark channel prior based image dehazing algorithms <s> Introduction <s> Outdoor imaging is plagued by poor visibility conditions due to atmospheric scattering, particularly in haze. A major problem is spatially-varying reduction of contrast by stray radiance (airlight), which is scattered by the haze particles towards the camera. Recent computer vision methods have shown that images can be compensated for haze, and even yield a depth map of the scene. A key step in such a scene recovery is subtraction of the airlight. In particular, this can be achieved by analyzing polarization-filtered images. However, the recovery requires parameters of the airlight. These parameters were estimated in past studies by measuring pixels in sky areas. This paper derives an approach for blindly recovering the parameter needed for separating the airlight from the measurements, thus recovering contrast, with neither user interaction nor existence of the sky in the frame. This eases the interaction and conditions needed for image dehazing, which also requires compensation for attenuation. The approach has proved successful in experiments, some of which are shown here. <s> BIB003 </s> A review on dark channel prior based image dehazing algorithms <s> Introduction <s> Bad weather, such as fog and haze, can significantly degrade the visibility of a scene. Optically, this is due to the substantial presence of particles in the atmosphere that absorb and scatter light. In computer vision, the absorption and scattering processes are commonly modeled by a linear combination of the direct attenuation and the airlight. Based on this model, a few methods have been proposed, and most of them require multiple input images of a scene, which have either different degrees of polarization or different atmospheric conditions. This requirement is the main drawback of these methods, since in many situations, it is difficult to be fulfilled. To resolve the problem, we introduce an automated method that only requires a single input image. This method is based on two basic observations: first, images with enhanced visibility (or clear-day images) have more contrast than images plagued by bad weather; second, airlight whose variation mainly depends on the distance of objects to the viewer, tends to be smooth. Relying on these two observations, we develop a cost function in the framework of Markov random fields, which can be efficiently optimized by various techniques, such as graph-cuts or belief propagation. The method does not require the geometrical information of the input image, and is applicable for both color and gray images. <s> BIB004 </s> A review on dark channel prior based image dehazing algorithms <s> Introduction <s> In landscape photography, distant objects often appear blurred with a blue color cast, a degradation caused by atmospheric haze. To enhance image contrast, pleasantness and information content, dehazing can be performed. <s> BIB005 </s> A review on dark channel prior based image dehazing algorithms <s> Introduction <s> Image dehazing is an important problem of common concern in image processing and computer vision areas. Recently, most researchers have adopted physical model based image dehazing to remove haze, in which dark channel prior law has achieved very good results in dealing with single images of outdoor scenes for image dehazing. Applying the theory of dark channel prior to estimate the atmosphere light A is a reliable method. Based on these, this paper studies five typical dehazing algorithms based on physical model and compares the advantages and disadvantages of these algorithms through a plenty of simulation experiments. It is some reference value to the researches in this filed with our studies. <s> BIB006 </s> A review on dark channel prior based image dehazing algorithms <s> Introduction <s> This paper presents an outdoors laser-based pedestrian tracking system using a group of mobile robots located near each other. Each robot detects pedestrians from its own laser scan image using an occupancy-grid-based method, and the robot tracks the detected pedestrians via Kalman filtering and global-nearest-neighbor (GNN)-based data association. The tracking data is broadcast to multiple robots through intercommunication and is combined using the covariance intersection (CI) method. For pedestrian tracking, each robot identifies its own posture using real-time-kinematic GPS (RTK-GPS) and laser scan matching. Using our cooperative tracking method, all the robots share the tracking data with each other; hence, individual robots can always recognize pedestrians that are invisible to any other robot. The simulation and experimental results show that cooperating tracking provides the tracking performance better than conventional individual tracking does. Our tracking system functions in a decentralized manner without any central server, and therefore, this provides a degree of scalability and robustness that cannot be achieved by conventional centralized architectures. <s> BIB007 </s> A review on dark channel prior based image dehazing algorithms <s> Introduction <s> AbstractIn this paper, reported algorithms for the removal of fog are reviewed. Fog reduces the visibility of scene and thus performance of various computer vision algorithms which use feature information. Formation of fog is the function of the depth. Estimation of depth information is under constraint problem if single image is available. Hence, removal of fog requires assumptions or prior information. Fog removal algorithms estimate the depth information with various assumptions, which are discussed in detail here. Fog removal algorithm has a wide application in tracking and navigation, consumer electronics, and entertainment industries. <s> BIB008 </s> A review on dark channel prior based image dehazing algorithms <s> Introduction <s> One source of accidents when driving a vehicle is the presence of fog. Fog fades the colors and reduces the contrasts in the scene with respect to their distances from the driver. Various camera-based Advanced Driver Assistance Systems (ADAS) can be improved if efficient algorithms are designed for visibility enhancement in road images. The visibility enhancement algorithm proposed in [1] is not optimized for road images. In this paper, we reformulate the problem as the inference of the local atmospheric veil from constraints. The algorithm in [1] thus becomes a particular case. From this new derivation, we propose to better handle road images by introducing an extra constraint taking into account that a large part of the image can be assumed to be a planar road. The advantages of the proposed local algorithm are the speed, the possibility to handle both color and gray-level images, and the small number of parameters. A new scheme is proposed for rating visibility enhancement algorithms based on the addition of several types of generated fog on synthetic and camera images. A comparative study and quantitative evaluation with other state-of-the-art algorithms is thus proposed. This evaluation demonstrates that the new algorithm produces better results with homogeneous fog and that it is able to deal better with the presence of heterogeneous fog. Finally, we also propose a model allowing to evaluate the potential safety benefit of an ADAS based on the display of defogged images. <s> BIB009 </s> A review on dark channel prior based image dehazing algorithms <s> Introduction <s> In visual surveillance of both humans and vehicles, a video stream is processed to characterize the events of interest through the detection of moving objects in each frame. The majority of errors in higher-level tasks such as tracking are often due to false detection. In this paper, a novel method is introduced for the detection of moving objects in surveillance applications which combines adaptive filtering technique with the Bayesian change detection algorithm. In proposed method, an adaptive structure firstly detects the edges of motion objects. Then, Bayesian algorithm corrects the shape of detected objects. The proposed method exhibits considerable robustness against noise, shadows, illumination changes, and repeated motions in the background compared to earlier works. In the proposed algorithm, no prior information about foreground and background is required and the motion detection is performed in an adaptive scheme. Besides, it is shown that the proposed algorithm is computationally efficient so that it can be easily implemented for online surveillance systems as well as similar applications. <s> BIB010
Due to absorption and scattering by atmospheric particles in haze, outdoor images have poor visibility under inclement weather. Poor visibility negatively impacts not only consumer photography but also computer vision applications for outdoor environments, such as object detection BIB010 and video surveillance BIB007 . Haze removal, which is referred to as dehazing, is considered an important process because haze-free images are visually pleasing and can significantly improve the performance of computer vision tasks. Methods presented in earlier studies had required multiple images to perform dehazing. For example, polarization-based methods  BIB002 BIB003 use the polarization property of scattered light to restore the scene depth information from two or more images taken with different degrees of polarization. Similarly, in  BIB001 , multiple images of the same scene are captured under different weather conditions to be used as reference images with clear weather conditions. However, these methods with multiple reference images have limitation in online image dehazing applications  BIB001 and may need a special imaging sensor BIB010 BIB007  . This leads the researchers to focus the dehazing method with a single reference image. Single image based methods rely on the typical characteristics of haze-free images. Tan BIB004 proposed a method that takes into account the characteristic that a haze-free image has a higher contrast than a hazy image. By maximizing the local contrast of the input hazy image, it enhances the visibility but introduces blocking artifacts around depth discontinuities. Fattal  proposed a method that infers the medium transmission by estimating the albedo of the scene. The underlying assumption is that the transmission and surface shading are locally uncorrected, which does not hold under a dense haze. Observing the property of haze-free outdoor images, He  proposed a novel prior-dark channel prior (DCP). The DCP is based on the property of "dark pixels," which have a very low intensity in at least one color channel, except for the sky region. Owing to its effectiveness in dehazing, the majority of recent dehazing techniques have adopted the DCP. The DCP-based dehazing techniques are composed of four major steps: atmospheric light estimation, transmission map estimation, transmission map refinement, and image reconstruction. In this paper, we perform an in-depth analysis of the DCP-based methods in the four-step point of view. We note that there are several review papers on image dehazing or defogging BIB006 BIB008    BIB009 . In BIB006 , five physical model-based dehazing algorithms are compared. In BIB008  , several enhancement-based and restorationbased defogging methods are investigated. In  , fog removal algorithms that use depth and prior information are analyzed. In  , a comparative study on the four representative dehazing methods BIB002   BIB005 are performed. In BIB009 , many visibility enhancement techniques developed for homogeneous and heterogeneous fog are discussed. To the best of our knowledge, our paper is the first one dedicated to DCP-based methods. This survey is expected to ascertain researchers' endeavors toward improving the original DCP method. The rest of the paper is organized as follows. In Section 1.2, the original DCP-based dehazing method is first reviewed. Section 1.3 provides an in-depth survey of conventional DCP-based methods. Section 1.4 discusses the performance evaluation methods for image dehazing, and Section 1.5 concludes the paper.