You are presented with multiple paper abstracts, alongside the associated literature review and chapter titles. Given the provided information, you are going to do a literature review. Please generate a coherent literature review that synthesizes the content, draws connections between different works, highlights major findings, and identifies any gaps in the existing research.

Literature Review Title: A review on dark channel prior based image dehazing algorithms 
Chapter Title:  Dark channel construction 
1. Abstract of Cited Paper (BIB001):  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. 
2. Abstract of Cited Paper (BIB002):  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. 
3. Abstract of Cited Paper (BIB003):  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. 
4. Abstract of Cited Paper (BIB004):  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. 
5. Abstract of Cited Paper (BIB005):  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. 
6. Abstract of Cited Paper (BIB006):  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. 
7. Abstract of Cited Paper (BIB007):  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 
8. Abstract of Cited Paper (BIB008):  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. 
9. Abstract of Cited Paper (BIB009):  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. 
10. Abstract of Cited Paper (BIB010):  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.

Literature Review:
Most conventional DCP-based dehazing methods estimate the dark channel from the input hazy image I. In Eq. (4), the size of the local patch Ω(x) is the only parameter that needs to be determined. Although the effect of the size of the local patch is significant, most conventional methods simply use a local patch with a fixed size or do not specify the size of the local patch. Table 2 shows typical patch sizes used in the previous methods. Figure 5a shows two hazy images. The top row in Fig. 5 corresponds to a remote aerial photograph with less local texture and heavy haze. Therefore, a small local (i) BIB011 Transmission map refinement Gaussian filter BIB002  BIB005 Bilateral filter BIB001 BIB006 BIB003 Soft matting  BIB001 Cross-bilateral filter BIB007 BIB004 Guided filter BIB008  BIB009 (i) t x ð Þ ¼ 1−w log min c∈ r;g;b (4) inevitably decreases the apparent resolution of the dark channel as the size of the patch increases. Therefore, the minimum possible patch size that does not produce false textures in the dark channel needs to be found for every hazy image by considering application-dependent image local details. Apart from the aforementioned general method for the dark channel estimation, Zhang BIB010 replaced the minimum operator by the median operator as follows: As a result of the median operation, the dark channels become less blurry, as shown in Fig. 6 . However, the median operator is computationally more complex than the minimum operator. Moreover, the median-based method is less physically meaningful because the assumption of the DCP becomes deteriorated. As shown in the second row of Fig. 6 , dense image textures remain visible for the dark channel, even when a large patch size of 15 × 15 is used. For the sake of the visibility enhancement of hazy images, however, the median filter is somewhat effective because it does not require complicated post-processing, which is necessary for smooth and blurry dark channels that are obtained by the minimum operator.