A review on dark channel prior based image dehazing algorithms <s> Dark channel prior (DCP) <s> In this paper, we propose a simple but effective image prior - dark channel prior to remove haze from a single input image. The dark channel prior is a kind of statistics of the haze-free outdoor images. It is based on a key observation - most local patches in haze-free outdoor images contain some pixels which have very low intensities in at least one color channel. Using this prior with the haze imaging model, we can directly estimate the thickness of the haze and recover a high quality haze-free image. Results on a variety of outdoor haze images demonstrate the power of the proposed prior. Moreover, a high quality depth map can also be obtained as a by-product of haze removal. <s> BIB001 </s> A review on dark channel prior based image dehazing algorithms <s> Dark channel prior (DCP) <s> A novel color image contrast enhancement approach is proposed that equalizes color components dependently using cooccurrence histogram. And then the enhanced color components are the trade-off between co-occurrence histogram equalization and dark channel prior. Histogram equalization (HE) and its derivatives are the most common used methods. They don't consider the spatial gray-level correlations so that the equalizations are applied for all gray-levels appeared in an image. However, in the fact, it will be more effective to equalize a gray-level with its spatial-adjacent gray-levels. Co-occurrence histogram equalization (COHE) for image contrast enhancement is proposed to overcome this problem. It is applied here to the RGB color space. But dependent COHE perhaps result in hue changes and artifacts. In this paper dark channel prior is used to solve this problem. Experimental results demonstrate that the proposed method can enhance the RGB images effectively. <s> BIB002 </s> A review on dark channel prior based image dehazing algorithms <s> Dark channel prior (DCP) <s> Outdoor photography and computer vision tasks often suffer from bad weather conditions, observed objects lose visibility and contrast due to the presence of atmospheric haze, fog, and smoke. In this paper, we propose a new method for real-time image and video dehazing. Based on a newly presented haze-free image prior - dark channel prior and a common haze imaging model, for a single input image, we can estimate the global atmospheric light and extract the scene objects transmission. To prevent artifacts, we refine the transmission using a cross-bilateral filter, and finally the haze-free frame can be restored by inversing the haze imaging model. The whole process is highly parallelized, and can be easily implemented on modern GPUs to achieve real-time performance. Comparing with existing methods, our approach provides similar or better results with much less processing time. The proposed method can be further used for many applications such as outdoor surveillance, remote sensing, and intelligent vehicles. In addition, rough depth information of the scene can be obtained as a by-product. <s> BIB003 </s> A review on dark channel prior based image dehazing algorithms <s> Dark channel prior (DCP) <s> Imaging in poor weather is often severely degraded by scattering due to suspended particles in the atmosphere such as haze, fog and mist. Poor visibility becomes a major problem for most outdoor vision applications. In this paper, we propose a novel fast defogging method from a single image of a scene based on a fast bilateral filtering approach. The complexity of our method is only a linear function of the number of input image pixels and this thus allows a very fast implementation. Results on a variety of outdoor foggy images demonstrate that our method achieves good restoration for contrast and color fidelity, resulting in a large improvement in image visibility. <s> BIB004 </s> A review on dark channel prior based image dehazing algorithms <s> Dark channel prior (DCP) <s> Dark channel prior is a kind of statistics of the haze-free outdoor images, which is widely used in image defogging. But when the image contains a large bright region, such as sky and white object, the prior will cause color distortion in these bright regions because of the underestimated transmission. To solve this problem, a video defogging technology based on adaptive tolerance is presented in this paper, and it is applied to video defogging by combining with the guided filter. First, the transmission of each video frame is estimated according to the dark channel prior, and then it is fast refined by the guided filter for restoration. If a large bright region exists in the video frame, the transmission of these regions will be corrected according to the adaptive tolerance, which avoids the color distortion in the video defogging. For the video of dynamic scenes which are caused by camera motion, each frame is defogged as a single image. But for the video of static scenes whose background is almost invariant, the transmission of the background is estimated and used for the defogging of all the frames instead of estimating the transmission of each frame. In this way, the rate of video defogging is greatly improved. Experimental results show that the algorithm has a strong applicability, and the proposed method can be further used for many applications, such as outdoor surveillance, remote sensing and intelligent vehicles. DOI: http://dx.doi.org/10.11591/telkomnika.v10i7.1556 Full Text: PDF <s> BIB005 </s> A review on dark channel prior based image dehazing algorithms <s> Dark channel prior (DCP) <s> In the frog and haze climatic condition, the captured picture will become blurred and the color is partial gray and white, due to the effect of atmospheric scattering. This situation brings a great deal of inconvenience to the video surveillance system, so the study of defogging algorithm in this weather is of great importance. This paper deeply analyzes the physical process of imaging in foggy weather. After full study on the haze removal algorithm of single image over the last decade, we propo se a fast haze removal algorithm which based on a fast bilateral filtering combined with dark colors prior. This algorithm starts with the atmospheric scattering model, derives a estimated transmission map by using dark channel prior, and then combines with grayscale to extract refined transmission map by using the fast bilateral filter. This algorithm has a fast execution speed and greatly improves the original algorithm which is morre time-consuming. On this basis, we analyzed the reasons why the image is dim after the haze removal using dark channel prior, and proposed the improved transmission map formula. Experimental-results show that this algorithm is feasible which effectively restores the contrast and color of the scene, significantly improves the visual effects of the image. Those image with large area of sky usually prone to distortion when using the dark channel prior, Therefore we propose a method of weakening the sky region, aims to improve the adaptability of the algorithm. <s> BIB006 </s> A review on dark channel prior based image dehazing algorithms <s> Dark channel prior (DCP) <s> In this paper, we propose a new fast dehazing method from single image based on filtering. The basic idea is to compute an accurate atmosphere veil that is not only smoother, but also respect with depth information of the underlying image. We firstly obtain an initial atmosphere scattering light through median filtering, then refine it by guided joint bilateral filtering to generate a new atmosphere veil which removes the abundant texture information and recovers the depth edge information. Finally, we solve the scene radiance using the atmosphere attenuation model. Compared with exiting state of the art dehazing methods, our method could get a better dehazing effect at distant scene and places where depth changes abruptly. Our method is fast with linear complexity in the number of pixels of the input image; furthermore, as our method can be performed in parallel, thus it can be further accelerated using GPU, which makes our method applicable for real-time requirement. <s> BIB007 </s> A review on dark channel prior based image dehazing algorithms <s> Dark channel prior (DCP) <s> Remote sensing images are widely used in various fields. However, they always suffer from the bad weather conditions which also affect their sufficient usage. In order to solve this problem, we propose a simple but effective method to remove haze from a single remote sensing image. Our work is based on the dark channel prior and a common haze imaging model. To eliminate artifacts, we use a low-pass Gaussian filter to refine the coarse estimated atmospheric veil. The experimental results show that our approach achieves good results with very little processing time. <s> BIB008 </s> A review on dark channel prior based image dehazing algorithms <s> Dark channel prior (DCP) <s> Poor visibility in bad weather, such as haze and ::: fog, is a major problem for many applications of computer vision. Thus, haze ::: removal is highly required for receiving high performance of the vision ::: algorithm. In this paper, we propose a new fast dehazing method for real-time ::: image and video processing. The transmission map estimated by an improved ::: guided filtering scheme is smooth and respect with depth information of the ::: underlying image. Results demonstrate that the proposed method achieves good ::: dehazeing effect as well as real-time performance. The proposed algorithm, due to its speed and ability to improve visibility, may ::: be used with advantages as pre-processing in many systems ranging from ::: surveillance, intelligent vehicles, to remote sensing. <s> BIB009 </s> A review on dark channel prior based image dehazing algorithms <s> Dark channel prior (DCP) <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> BIB010 </s> A review on dark channel prior based image dehazing algorithms <s> Dark channel prior (DCP) <s> In this paper, we propose a novel method for estimating the transmission and removing the haze from single image. This method is based on observation that the property of haze is widely spread. Thus its estimated transmission should be smoothly changed over the scene. We employed the local entropy and log function for estimating the atmospheric light and the smooth transmission. Experimental results demonstrated that the proposed method can be applied efficiently to outdoor vision applications or devices including cameras and camcorders with low complexity. <s> BIB011 </s> A review on dark channel prior based image dehazing algorithms <s> Dark channel prior (DCP) <s> The dark channel prior is generally a powerful prior for single image dehazing, but it is invalid when there are objects which have similar colors to the atmospheric light. It is difficult to find the accurate transmission factor in this case, and thus the results show some color distortion. This paper proposes a new algorithm for the estimation of correct transmission map regardless of object color. The proposed method defines a reliability map that depicts how much the objects or areas meet the dark channel prior assumption, and then estimate the transmission map using the reliable pixels only. The transmission factors at the unreliable pixels are estimated by linear fitting using the reliable neighboring pixels. Experiments show that the dehazed images by the proposed method have less color distortion than the results by the conventional algorithm. <s> BIB012 </s> A review on dark channel prior based image dehazing algorithms <s> Dark channel prior (DCP) <s> Outdoor images captured during inclement weather conditions generally exhibit visibility degradation. Localized light sources often result from activation of streetlights and vehicle headlights and are common scenarios in these conditions. The presence of localized light sources in hazy images may cause the generation of over saturation artifacts when those images are restored by traditional state-of-the-art haze removal techniques. Therefore, we propose a novel haze removal approach based on the proposed hybrid dark channel prior technique in order to remedy the problems associated with localized light sources during image restoration. The overall results show that the proposed haze removal approach can recover haze-free images more effectively than can the other previous state-of-the-art haze removal approach while avoiding over-saturation. <s> BIB013 </s> A review on dark channel prior based image dehazing algorithms <s> Dark channel prior (DCP) <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> BIB014 </s> A review on dark channel prior based image dehazing algorithms <s> Dark channel prior (DCP) <s> The visibility of outdoor images captured in inclement weather is often degraded due to the presence of haze, fog, sandstorms, and so on. Poor visibility caused by atmospheric phenomena in turn causes failure in computer vision applications, such as outdoor object recognition systems, obstacle detection systems, video surveillance systems, and intelligent transportation systems. In order to solve this problem, visibility restoration (VR) techniques have been developed and play an important role in many computer vision applications that operate in various weather conditions. However, removing haze from a single image with a complex structure and color distortion is a difficult task for VR techniques. This paper proposes a novel VR method that uses a combination of three major modules: 1) a depth estimation (DE) module; 2) a color analysis (CA) module; and 3) a VR module. The proposed DE module takes advantage of the median filter technique and adopts our adaptive gamma correction technique. By doing so, halo effects can be avoided in images with complex structures, and effective transmission map estimation can be achieved. The proposed CA module is based on the gray world assumption and analyzes the color characteristics of the input hazy image. Subsequently, the VR module uses the adjusted transmission map and the color-correlated information to repair the color distortion in variable scenes captured during inclement weather conditions. The experimental results demonstrate that our proposed method provides superior haze removal in comparison with the previous state-of-the-art method through qualitative and quantitative evaluations of different scenes captured during various weather conditions. <s> BIB015 </s> A review on dark channel prior based image dehazing algorithms <s> Dark channel prior (DCP) <s> Bad weather conditions such as fog, haze, and dust often reduce the performance of outdoor cameras. To improve the effectiveness of surveillance and in-vehicle cameras under such conditions, we propose a method based on a dark channel prior for quickly defogging images. It first estimates the intensity of the atmospheric light by searching the sky area in the foggy image. Then it estimates the transmission map by refining a coarse map from a fine map. Finally, it produces a clearer image from the foggy image by using the estimated intensity and the transmission map. When implemented on a notebook PC with a graphics processing unit (GPU), it was able to process 50 images (720 × 480 pixels) per second. It can thus be used for real-time processing of surveillance and in-vehicle system images. <s> BIB016 </s> A review on dark channel prior based image dehazing algorithms <s> Dark channel prior (DCP) <s> We propose a single image dehazing method that is based on a physical model and the dark channel prior principle. The selection of an atmospheric light value is directly responsible for the color authenticity and contrast of the resulting image. Our choice of atmospheric light is based on a variogram, which slowly weakens areas in the image that do not conform to the dark channel prior. Additionally, we propose a fast transmission estimation algorithm to shorten the processing time. Along with a subjective evaluation, the image quality was also evaluated using three indicators: MSE, PSNR, and average gradient. Our experimental results show that the proposed method can obtain accurate dehazing results and improve the operational efficiency. <s> BIB017
He et al.  performed an empirical investigation of the characteristic of haze-free outdoor images. They found that there are dark pixels whose intensity values are very close to zero for at least one color channel within an image patch. Based on this observation, a dark channel is defined as follows: where J c is an intensity for a color channel c ∈ {r, g, b} of the RGB image and Ω(x) is a local patch centered at pixel x. According to Eq. (4), the minimum value among the three color channels and all pixels in Ω(x) is chosen as the dark channel J dark (x). From 5000 dark channels of outdoor haze-free images, it was demonstrated that about 75 percent of the pixels in the dark channels have zero values and 90 percent of the pixels have values below 35 when the pixels in the sky region are excluded  . The low intensities in the dark channel are due to the following three main features: (i) shadows, e.g., shadows from cars and buildings in an urban scene or shadows from trees, leaves, and rocks in a landscape (Fig. 2a) ; (ii) colorful objects or surfaces, e.g., red or yellow flowers and leaves (Fig. 2b) ; and (iii) dark objects or surfaces, e.g., dark tree trunks and stones (Fig. 2c) . Based on the above observation, the pixel value at the dark channel can be approximated as follows:  This approximation to zero for the pixel value of the dark channel is called the DCP. On the contrary, the dark channels from hazy images produce pixels that have values far above zero as shown in Fig. 3 . Global atmospheric light tends to be achromatic and bright, and a mixture of airlight and direct attenuation significantly increases the minimum value of the three color channels in the local patch. This implies that the pixel values of the dark channel can serve as an important clue to estimate the haze density. Successful dehazing results of various DCP-based dehazing algorithms  BIB001 BIB015 BIB005 BIB006 BIB016 BIB007 BIB002  BIB008 BIB003 BIB011 BIB009 BIB010 BIB004 BIB012 BIB013 BIB014 BIB017 support the effectiveness of the DCP in image dehazing.