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:  Atmospheric light estimation 
1. Abstract of Cited Paper (BIB001):  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. 
2. Abstract of Cited Paper (BIB002):  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. 
3. Abstract of Cited Paper (BIB003):  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 
4. Abstract of Cited Paper (BIB004):  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. 
5. Abstract of Cited Paper (BIB005):  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. 
6. Abstract of Cited Paper (BIB006):  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. 
7. Abstract of Cited Paper (BIB007):  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. 
8. Abstract of Cited Paper (BIB008):  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. 
9. Abstract of Cited Paper (BIB009):  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. 
10. Abstract of Cited Paper (BIB010):  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. 
11. Abstract of Cited Paper (BIB011):  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.

Literature Review:
The majority of conventional DCP-based dehazing methods estimate A as described in Section 1.2.3. In BIB003 BIB002 , the pixel with the highest dark channel value is used directly as follows: However, the above method can incorrectly select the pixel when the scene contains bright objects. Instead, pixels with a top p% dark channel values are selected as the most haze-opaque pixels, and the one with the highest intensity is used to estimate A. This remains one parameter p in the estimation of A, which is empirically set as 0.1  BIB001 BIB011 BIB004 BIB005 BIB012 or 0.2 BIB006 . In BIB008 , to explicitly exclude bright objects from the estimation of A, the local entropy is measured as where p x (i) represents the probability of a pixel value i in the local patch centered at x, and N represents the maximum pixel value. The local entropy value is low for regions with smooth variations, which highly likely correspond to haze-opaque regions. Therefore, the pixel with the lowest entropy value is used to obtain A among the highest p% pixels in the dark channel (p = 0.1 BIB008 ). Table 3 lists the conventional methods that are used to estimate atmospheric light. To quantitatively evaluate atmospheric light estimation methods, we used the foggy road image database (FRIDA) BIB007 which consists of pairs of synthetic color and depth images. For a given depth image and β, the ground-truth transmission map can be constructed as t(x) = e − βd(x) . The hazy image I is 11 × 11 BIB002 15 × 15  BIB008 BIB009 then obtained as Eq. (6) by using the atmospheric light A. Therefore, a variety of hazy images can be generated by changing β (haze density) and A (global lightness). Figure 7 shows the average root-mean-square error (RMSE) between the ground-truth and estimated atmospheric lights for the 66 test images in the FRIDA. The RMSE is obtained as where represent the ground-truth and estimated atmospheric lights, respectively. Since the candidate pixels for the atmospheric light estimation are obtained from the dark channel, the local patch size also plays an important role in the accuracy of the estimation. When a small patch size is used, as shown in Fig. 8b , the pixels for bright objects are considered as candidate pixels, yielding inaccurate A estimates. The use of a large patch size can prevent selecting such pixels, as shown in Fig. 8c . The quantitative evaluation result as shown in Fig. 7a also supports our observation. The accuracy is rather insensitive to p when a large 32 × 32 patch is used. Therefore, a large patch size (e.g., 32 × 32) with p = 0~0.4 % is effective only when the accuracy of the atmospheric light estimation is considered. One practical solution that takes into account the accuracy of both the dark channel and the atmospheric light involves using different patch sizes to estimate the dark channel estimation and atmospheric light BIB010 . When the local entropy, as in Eq. (15), is used to prevent pixels of small bright objects from being selected, the estimation accuracy of the atmospheric light improves, as shown in Fig. 7b BIB008 . The estimation accuracy is still best for the largest patch size of 32 × 32 and is less sensitive to the p value due to the robustness of candidate pixel selection.