A review on dark channel prior based image dehazing algorithms <s> Guided filter <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> BIB001 </s> A review on dark channel prior based image dehazing algorithms <s> Guided filter <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> BIB002 </s> A review on dark channel prior based image dehazing algorithms <s> Guided filter <s> In this paper, we propose a novel explicit image filter called guided filter. Derived from a local linear model, the guided filter computes the filtering output by considering the content of a guidance image, which can be the input image itself or another different image. The guided filter can be used as an edge-preserving smoothing operator like the popular bilateral filter [1], but it has better behaviors near edges. The guided filter is also a more generic concept beyond smoothing: It can transfer the structures of the guidance image to the filtering output, enabling new filtering applications like dehazing and guided feathering. Moreover, the guided filter naturally has a fast and nonapproximate linear time algorithm, regardless of the kernel size and the intensity range. Currently, it is one of the fastest edge-preserving filters. Experiments show that the guided filter is both effective and efficient in a great variety of computer vision and computer graphics applications, including edge-aware smoothing, detail enhancement, HDR compression, image matting/feathering, dehazing, joint upsampling, etc. <s> BIB003 </s> A review on dark channel prior based image dehazing algorithms <s> Guided filter <s> Single Image Dehazing technology is widely needed in many fields. In order to solve the problem, we propose an improved and modified framework for estimating the optical transmission t in hazy scenes in a given single input image. At first, a novel formulation to the t estimation is presented with the combination of constant albedo and dark channel prior knowledge. Later, we introduce the watershed segmentation methodology into the algorithm to separate the image into some gray level consistent parts based on the original image’s color distribution and feature difference. As a result, we could estimate the atmospheric light A better and avoid the important drawback of artifacts phenomenon. At last, through this effective estimation to t and A, the scene visibility is largely increased and the haze-free scene contrasts can be better recovered. The experimental analysis shows that compared with other state-of-the-art algorithms, our proposed algorithm can provide promising results to dark channel prior and get corresponding reliable estimation value t with the advantage of minimal halo artifacts and fewer unreal details. Our method is more effective and robust. <s> BIB004 </s> A review on dark channel prior based image dehazing algorithms <s> Guided filter <s> The blurred image restoration method can dramatically highlight the image details and enhance the global contrast, which is of benefit to improvement of the visual effect during practical applications. This paper is based on the dark channel prior principle and aims at the prior information absent blurred image degradation situation. A lot of improvements have been made to estimate the transmission map of blurred images. Since the dark channel prior principle can effectively restore the blurred image at the cost of a large amount of computation, the total variation (TV) and image morphology transform (specifically top-hat transform and bottom-hat transform) have been introduced into the improved method. Compared with original transmission map estimation methods, the proposed method features both simplicity and accuracy. The estimated transmission map together with the element can restore the image. Simulation results show that this method could inhibit the ill-posed problem during image restoration, meanwhile it can greatly improve the image quality and definition. <s> BIB005 </s> A review on dark channel prior based image dehazing algorithms <s> Guided filter <s> Images and videos taken in foggy weather often suffer from low visibility. Recent studies demonstrate the effectiveness of dark channel prior [3] and guided filter [4] based approaches for image dehazing. However, these methods require high computational cost which makes them infeasible for realtime and embedding systems. In this paper, we propose Edge-Guided Interpolated Filter (EGIF) for fast image and video dehazing. The main contributions are twofold. Firstly, we develop Guided Interpolated Filter (GIF) to significantly speed up the estimation of transmission map, which is the most computational cost step in previous methods. Secondly, we utilize edge map as guidance image in GIF to enhance the fine details in dehazed images. Experimental results show that GIF can largely improve the computational efficiency and achieve comparable dehazing performance as previous guided filter based methods. EGIF can further enhance the sharpness of transmission map. Our method can achieve real-time processing for image of size 1024 × 768 with single CPU core (2GHz). <s> BIB006 </s> A review on dark channel prior based image dehazing algorithms <s> Guided filter <s> Image has important applications in many fields such as marine surveillance, environment monitoring and so on. The scattering effects of the atmospheric particles in the air play a main role of resulting in contrast reduction and color fading. For dealing with this challenging but imperative issue, there are numerous researchers have strove for this scientific field and published a plenty of findings about restoring the foggy image. In generally, the foggy image always includes the sky and non-sky regions while the pixel values in this two distinguished regions is extremely different. The dark channel prior algorithm has been considered as one effective dehazing approach which only employs one constant factor for the overall image regardless of the scene pattern. This imprudent procedures always leads to more darkness image color and fails to achieve excellent results. For dealing with this challenging but imperative issue, we propose one improved dark channel prior dehazing approach using adaptive factor. In our algorithm, the foggy image is segmented into sky region and non-sky region respectively, the critical parameters i.e. light intensity and transmission ratio are obtained based on different factors. Some comparative experiments have also been conducted for validating dehazing performance of the proposed approach. <s> BIB007 </s> A review on dark channel prior based image dehazing algorithms <s> Guided filter <s> The authors propose a novel and efficient method for single image dehazing. To accelerate the transmission estimation process, a block-to-pixel interpolation method is used for fine dark channel computation, in which the block-level dark channel is first computed, and then the fine pixel-level dark channel is obtained by a weighted voting of the block-level dark channel to preserve edges and smooth out texture noise. This technique can be used for a direct transmission map generation without a computationally expensive refinement step. Since the dark channel prior (DCP) is not valid in bright (sky) regions, they propose an adaptive DCP modelled by a Gaussian curve that produces a more natural recovered image of the sky and other bright regions. In addition, a scaling method for transmission map computation is proposed to further accelerate the dehazing method. Through experiments, they show that the proposed adaptive block-to-pixel technique is about 30 times faster and produces improved recovered images than the well-known state-of-the-art DCP approach. <s> BIB008
To speed up the transmission map refinement, the authors of the original DCPbased dehazing method  replaced the soft Fig. 18 The result of the cross-bilateral filter. a Hazy images. b Transmission map obtained using the local patch with the size 3 × 3. c Cross-bilateral filtered transmission map using σ s = 15 and σ r = 0.1. d Transmission map obtained using the local patch with the size 15 × 15. e Cross-bilateral-filtered transmission map using σ r = 15 and σ r = 0.1 matting to the guided filter BIB001  BIB002 BIB003 . The guided filter also uses the hazy image I as a guidance, but its novelty lies in adopting the linear model as follows: where the coefficients a x and b x are assumed to be constant in Ω x and are derived by minimizing the following energy: where ε is a regularization parameter penalizing large a x . The solution (a x , b x ) can be obtained as e Guided filtered transmission maps using ε = 0.01 where μ x and σ 2 x re the mean and variance of the guidance image I in window Ω x , respectively. |w| denotes the number of pixels in Ω x and t x ð Þ ¼ Considering the overlapping windows in calculating a x and b x , the final refined transmission mapt x ð Þ is obtained aŝ where the average of all the coefficients obtained at pixel x. Figure 20 shows the result of the guided filter. Since the refined transmission map is fully obtained from the hazy image, the resultant map contains the similar level of sharpness of the hazy image without yielding significant false color textures. Figure 21 shows the quantitative performance of the guided filter with different ε values. As ε increases, the transmission map becomes smooth, and thus, a proper selection of ε is significant. In our experiments using the FRIDA, ε of 0.01 produced the smallest RMSE value regardless of the size of the local patch. 1.3.4.6 Auxiliary methods for transmission map enhancement Recent efforts are made to enhance the transmission map BIB004 BIB005 BIB006 BIB007 BIB008 , which can be categorized into three different approaches. The first approach is to use the transmission map obtained at low resolution BIB006 BIB008 . In BIB006 , the guided filter is performed at low resolution and the filter coefficients at the original resolution are obtained using bilinear interpolation, which enables a speedup of the transmission map refinement. In BIB008 , non-overlapping patches with the size 10 × 10 are used to obtain a very low resolution transmission map, and then, it is combined with a very high-resolution transmission map obtained using Ω(x) = x in Eq. (9). This combination scheme can make the transmission map refinement unnecessary. In the second approach, the transmission map enhancement can be achieved by applying a preprocessing filter to the hazy image. In BIB005 , the total variation based image restoration and morphological filtering are applied to the hazy image, which can prevent producing unnecessary texture details from the estimated transmission map. In BIB006 , an edge-enhanced hazy image is used as a guidance image at the guided filtering step to reconstruct the transmission map with sharp edges. The third approach is to estimate the transmission map not from rectangular patches but from segments. In BIB004 , the watershed segmentation is performed to extract regions that the transmission can be reliably estimated. In BIB007 , a gray-level thresholding is performed to divide an image into sky and non-sky regions and transmission maps are then separately estimated for the two regions. Since blurry transmission maps are originated from rectangular patch-wise processing in Eq. (9), these segmentation-based methods tend to produce sharp transmission maps without further refinement.