A review on dark channel prior based image dehazing algorithms <s> Performance evaluation methods <s> One source of difficulties when processing outdoor images is the presence of haze, fog or smoke which fades the colors and reduces the contrast of the observed objects. We introduce a novel algorithm and variants for visibility restoration from a single image. The main advantage of the proposed algorithm compared with other is its speed: its complexity is a linear function of the number of image pixels only. This speed allows visibility restoration to be applied for the first time within real-time processing applications such as sign, lane-marking and obstacle detection from an in-vehicle camera. Another advantage is the possibility to handle both color images or gray level images since the ambiguity between the presence of fog and the objects with low color saturation is solved by assuming only small objects can have colors with low saturation. The algorithm is controlled only by a few parameters and consists in: atmospheric veil inference, image restoration and smoothing, tone mapping. A comparative study and quantitative evaluation is proposed with a few other state of the art algorithms which demonstrates that similar or better quality results are obtained. Finally, an application is presented to lane-marking extraction in gray level images, illustrating the interest of the approach. <s> BIB001 </s> A review on dark channel prior based image dehazing algorithms <s> Performance evaluation methods <s> The contrast of outdoor images acquired under adverse weather conditions, especially foggy weather, is altered by the scattering of daylight by atmospheric particles. As a consequence, different methods have been designed to restore the contrast of these images. However, there is a lack of methodology to assess the performances of the methods or to rate them. Unlike image quality assessment or image restoration areas, there is no easy way to have a reference image, which makes the problem not straightforward to solve. In this paper, an approach is proposed which consists in computing the ratio between the gradient of the visible edges between the image before and after contrast restoration. In this way, an indicator of visibility enhancement is provided based on the concept of visibility level, commonly used in lighting engineering. Finally, the methodology is applied to contrast enhancement assessment and to the comparison of tone-mapping operators. <s> BIB002 </s> A review on dark channel prior based image dehazing algorithms <s> Performance evaluation methods <s> The misty, foggy, or hazy weather conditions lead to image color distortion and reduce the resolution and the contrast of the observed object in outdoor scene acquisition. In order to detect and remove haze, this article proposes a novel effective algorithm for visibility enhancement from a single gray or color image. Since it can be considered that the haze mainly concentrates in one component of the multilayer image, the haze-free image is reconstructed through haze layer estimation based on the image filtering approach using both low-rank technique and the overlap averaging scheme. By using parallel analysis with Monte Carlo simulation from the coarse atmospheric veil by the median filter, the refined smooth haze layer is acquired with both less texture and retaining depth changes. With the dark channel prior, the normalized transmission coefficient is calculated to restore fogless image. Experimental results show that the proposed algorithm is a simpler and efficient method for clarity improvement and contrast enhancement from a single foggy image. Moreover, it can be comparable with the state-of-the-art methods, and even has better results than them. <s> BIB003 </s> A review on dark channel prior based image dehazing algorithms <s> Performance evaluation methods <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> BIB004 </s> A review on dark channel prior based image dehazing algorithms <s> Performance evaluation methods <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> BIB005 </s> A review on dark channel prior based image dehazing algorithms <s> Performance evaluation methods <s> Images of outdoor scenes are usually degraded under bad weather conditions, which results in a hazy image. To date, most haze removal methods based on a single image have ignored the effects of sensor blur and noise. Therefore, in this paper, a three-stage algorithm for haze removal, considering sensor blur and noise, is proposed. In the first stage, we preprocess the degraded image and eliminate the blur/noise interference to estimate the hazy image. In the second stage, we estimate the transmission and atmospheric light by the dark channel prior method. In the third stage, a regularized method is proposed to recover the underlying image. Experimental results with both simulated and real data demonstrate that the proposed algorithm is effective, based on both the visual effect and quantitative assessment. <s> BIB006 </s> A review on dark channel prior based image dehazing algorithms <s> Performance evaluation methods <s> In this letter we present a novel strategy to enhance images degraded by the atmospheric phenomenon of haze. Our single-based image technique does not require any geometrical information or user interaction enhancing such images by restoring the contrast of the degraded images. The degradation of the finest details and gradients is constrained to a minimum level. Using a simple formulation that is derived from the lightness predictor our contrast enhancement technique restores lost discontinuities only in regions that insufficiently represent original chromatic contrast of the scene. The parameters of our simple formulation are optimized to preserve the original color spatial distribution and the local contrast. We demonstrate that our dehazing technique is suitable for the challenging problem of image matching based on local feature points. Moreover, we are the first that present an image matching evaluation performed for hazy images. Extensive experiments demonstrates the utility of the novel technique. <s> BIB007
In Section 1.3, we reviewed the conventional DCP-based dehazing algorithms by dividing them into subcomponents and discussing various methods used in each subcomponent. Finally, we need to objectively evaluate the quality of the dehazed images. In this section, we first Ga Gaussian, Bi bilateral filter, Cr cross-bilateral filter, Gu guided filter Fig. 23 Comparison of time consumption for each dehazing step with different transmission map refinement methods study the existing metrics developed for evaluating the quality of dehazed images. Table 8 lists the metrics used for evaluating the quality of dehazed images. The most widely used metric is the ratio of visible edges between dehazed and hazy images (denoted as Q e ) BIB003 BIB004 BIB001 BIB002 . Since the dehazed image tends to have sharper details than the hazy image, it is considered that the higher the Q e value the better the dehazed image. In order to more precisely measure the local image sharpness, the ratio of visible edges' gradients between the dehazed and hazy images (denoted as Q g ) is also evaluated BIB003 BIB004 BIB001 BIB002 . In a similar manner, the higher the Q g value, the better the dehazed image. In BIB005 BIB001 BIB002 , the percentage of pixels which becomes completely black or completely white after dehazing (denoted as Q o ) is measured. As Q o accounts for the over-enhancement, the smaller the Q o value, the better the dehazed image. Other quality metrics developed for general image restoration problems such as image entropy BIB003 and Q-metric BIB006 are often directly used to evaluate the quality of dehazed images, which are not discussed in this section. One problem is that the reliability of the aforementioned metrics has not been verified yet. As the quality of the dehazed image is strongly dependent on the accuracy of the transmission map, we relate the RMSE (between the ground-truth and estimated transmission maps) and the quality metrics of image dehazing. Figure 26 compares the RMSE and quality metrics, where the red curves denote the fitted functions. Each point indicates the result for various haze density (β∈) ≤ [0.005, 0.015]. As can be seen, a general tendency of the quality metrics is consistent with their definitions (i.e., the RMSE value tends to decrease as Q e and Q g increases and vice versa with respect to Q o ). Figure 27 shows the case when Q e and Q g are not trustworthy. As can be seen in Fig. 27b, d , some false positive edges are detected and they tend to unnecessarily increase Q e and Q g values. Therefore, the dehazed images obtained using the Gaussian filter have even higher Q e and Q g values than those obtained using the soft matting (in Fig. 26 , Gaussian filter: Q e = 1.7311, Q g = 1.1073; soft matting: Q e = 1.1675, Q g = 0.8774). Therefore, Q e and Q g should be used considering the characteristics of the dehazed algorithms. More dedicated quality evaluation methods need to be developed for image dehazing. Lastly, an application-specific quality metric of image dehazing is also presented BIB007 . When image dehazing is developed for computer vision applications, it is expected that the dehazed image results in the performance enhancement of computer vision tasks such as object detection and recognition. Since detection and matching of feature points play an important role for such computer vision tasks, the numbers of matched feature points are compared between hazy and dehazed image pairs BIB007 . It is then assumed that the more the matched feature points, the better the image dehazing algorithm. We believe that other application-specific quality metrics can be devised in a similar manner.