Realtime dehazing using colour uniformity principle
01 Sep 2019-Iet Image Processing (The Institution of Engineering and Technology)-Vol. 13, Iss: 11, pp 1931-1939
TL;DR: The authors present a novel dehazing algorithm based on colour uniformity principle (CUP) which meets the desired requirements of a realtime implementation and produces reliable dehazed output in varying haze conditions, unlike current methods.
Abstract: Dehazing is an important process as it can significantly improve the performance of computer vision applications in outdoor environments. The two main requirements that an online dehazing system demands are low processing time and high visual range. The authors present a novel dehazing algorithm based on colour uniformity principle (CUP) which meets the desired requirements of a realtime implementation. Estimation of atmospheric scattering parameter and transmission map forms the key step in dehazing problem. At first, the authors use CUP to generate the transmission map and refine it further by Fast Guided Filter. They estimate the atmospheric scattering parameter with the help of the estimated transmission map. Experimental results show that the quality of dehazed output, produced in real-time using the proposed method, is comparable with the results achieved by the state of the art techniques. The proposed dehazing method produces reliable dehazed output in varying haze conditions, unlike current methods.
Citations
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TL;DR: In this paper, an end-to-end network for single image dehazing is proposed, which enhances the CycleGAN model by introducing a transformer architecture within the generator, which is specific for haze removal.
13 citations
TL;DR: In this paper, the authors proposed a new method to estimate the transmission map based on a mean channel prior (MCP), which represents the depth map to estimate a transmission map, and a deep neural network to identify the hazy image for the further dehazing process.
Abstract: Outdoor images are having several applications including autonomous vehicles, geo-mapping, and surveillance. It is a common phenomenon that the images captured outdoor are prone to noise, which arises due to natural and manmade extreme atmospheric conditions such as haze, fog, and smog. Importantly in autonomous vehicle navigation, it is very important to recover the ground truth image to get the better decision by the system. Estimation of the transmission map and air-light is very crucial in recovering the ground truth image. In this study, the authors proposed a new method to estimate the transmission map based on a mean channel prior (MCP), which represents the depth map to estimate the transmission map. The authors proposed a deep neural network to identify the hazy image for the further dehazing process. In this study, the authors presented, two novel contributions, first an MCP-based image dehazing and second, a deep neural network-based identification of hazy images as a pre-processing block in the proposed end to end system. The proposed deep learning network using the TensorFlow platform provided validation accuracy of 93.4% for hazy image classification. Finally, the proposed MCP-based dehazing network showed better performance in terms of peak-signal-to-noise ratio, structural similarity index, and computational time than that of existing methods.
3 citations
1 citations
Journal Article•
TL;DR: Experimental results with real images show that the embedded high speed image enhancement processing system can improve the contrast of the fog-degraded image,effectively enhance the color image, and the running time of the program is shorter, which can meet the project.
Abstract: An embedded high speed image enhancement processing system on high performance DSP TMS320C6418 and FPGA is designed,and a color image enhancement algorithm is presented. Firstly,the color image is transformed from RGB to HIS. Then,histogram equalization enhancement is adopted in the brightness image. Finally,the color image is transformed from RGB to HIS. Experimental results with real images show that the system can improve the contrast of the fog-degraded image,effectively enhance the color image,and the running time of the program is shorter,which can meet the project.
1 citations
References
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TL;DR: A simple but effective image prior - dark channel prior to remove haze from a single input image is proposed, 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.
Abstract: 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 outdoor haze-free images. It is based on a key observation-most local patches in outdoor haze-free images contain some pixels whose intensity is very low 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 hazy images demonstrate the power of the proposed prior. Moreover, a high-quality depth map can also be obtained as a byproduct of haze removal.
3,668 citations
TL;DR: DehazeNet as discussed by the authors adopts convolutional neural network-based deep architecture, whose layers are specially designed to embody the established assumptions/priors in image dehazing.
Abstract: Single image haze removal is a challenging ill-posed problem. Existing methods use various constraints/priors to get plausible dehazing solutions. The key to achieve haze removal is to estimate a medium transmission map for an input hazy image. In this paper, we propose a trainable end-to-end system called DehazeNet, for medium transmission estimation. DehazeNet takes a hazy image as input, and outputs its medium transmission map that is subsequently used to recover a haze-free image via atmospheric scattering model. DehazeNet adopts convolutional neural network-based deep architecture, whose layers are specially designed to embody the established assumptions/priors in image dehazing. Specifically, the layers of Maxout units are used for feature extraction, which can generate almost all haze-relevant features. We also propose a novel nonlinear activation function in DehazeNet, called bilateral rectified linear unit, which is able to improve the quality of recovered haze-free image. We establish connections between the components of the proposed DehazeNet and those used in existing methods. Experiments on benchmark images show that DehazeNet achieves superior performance over existing methods, yet keeps efficient and easy to use.
1,880 citations
01 Aug 2008
TL;DR: Results demonstrate the new method abilities to remove the haze layer as well as provide a reliable transmission estimate which can be used for additional applications such as image refocusing and novel view synthesis.
Abstract: In this paper we present a new method for estimating the optical transmission in hazy scenes given a single input image. Based on this estimation, the scattered light is eliminated to increase scene visibility and recover haze-free scene contrasts. In this new approach we formulate a refined image formation model that accounts for surface shading in addition to the transmission function. This allows us to resolve ambiguities in the data by searching for a solution in which the resulting shading and transmission functions are locally statistically uncorrelated. A similar principle is used to estimate the color of the haze. Results demonstrate the new method abilities to remove the haze layer as well as provide a reliable transmission estimate which can be used for additional applications such as image refocusing and novel view synthesis.
1,866 citations
TL;DR: A closed-form solution to natural image matting that allows us to find the globally optimal alpha matte by solving a sparse linear system of equations and predicts the properties of the solution by analyzing the eigenvectors of a sparse matrix, closely related to matrices used in spectral image segmentation algorithms.
Abstract: Interactive digital matting, the process of extracting a foreground object from an image based on limited user input, is an important task in image and video editing. From a computer vision perspective, this task is extremely challenging because it is massively ill-posed - at each pixel we must estimate the foreground and the background colors, as well as the foreground opacity ("alpha matte") from a single color measurement. Current approaches either restrict the estimation to a small part of the image, estimating foreground and background colors based on nearby pixels where they are known, or perform iterative nonlinear estimation by alternating foreground and background color estimation with alpha estimation. In this paper, we present a closed-form solution to natural image matting. We derive a cost function from local smoothness assumptions on foreground and background colors and show that in the resulting expression, it is possible to analytically eliminate the foreground and background colors to obtain a quadratic cost function in alpha. This allows us to find the globally optimal alpha matte by solving a sparse linear system of equations. Furthermore, the closed-form formula allows us to predict the properties of the solution by analyzing the eigenvectors of a sparse matrix, closely related to matrices used in spectral image segmentation algorithms. We show that high-quality mattes for natural images may be obtained from a small amount of user input.
1,851 citations
TL;DR: A simple but powerful color attenuation prior for haze removal from a single input hazy image is proposed and outperforms state-of-the-art haze removal algorithms in terms of both efficiency and the dehazing effect.
Abstract: Single image haze removal has been a challenging problem due to its ill-posed nature. In this paper, we propose a simple but powerful color attenuation prior for haze removal from a single input hazy image. By creating a linear model for modeling the scene depth of the hazy image under this novel prior and learning the parameters of the model with a supervised learning method, the depth information can be well recovered. With the depth map of the hazy image, we can easily estimate the transmission and restore the scene radiance via the atmospheric scattering model, and thus effectively remove the haze from a single image. Experimental results show that the proposed approach outperforms state-of-the-art haze removal algorithms in terms of both efficiency and the dehazing effect.
1,495 citations