Despite the recent progress in image dehazing, several problems remain largely unsolved such as robustness for varying scenes, the visual quality of reconstructed images, and effectiveness and flexibility for applications. To tackle these problems, we propose a new deep network architecture for single image dehazing called DR-Net. Our model consists of three main subnetworks: a transmission prediction network that predicts transmission map for the input image, a haze removal network that reconstructs latent image steered by the transmission map, and a refinement network that enhances the details and color properties of the dehazed result via weakly supervised learning. Compared to previous methods, our method advances in three aspects: (i) pure data-driven model; (ii) the end-to-end system; (iii) superior robustness, accuracy, and applicability. Extensive experiments demonstrate that our DR-Net outperforms the state-of-the-art methods on both synthetic and real images in qualitative and quantitative metrics. Additionally, the utility of DR-Net has been illustrated by its potential usage in several important computer vision tasks.
Supplementary information based methods usually require additional knowledge such as 3D geographical models @cite_39 , scene depth @cite_28 , multiple images of the scene under different weather conditions @cite_6 , polarization filters @cite_34 , and so forth. Nevertheless, these methods are mostly computationally intensive and not applicable to dynamic scenes. Much attention, therefore, has been devoted to single image dehazing methods @cite_18 @cite_0 @cite_23 @cite_29 @cite_5 @cite_1 @cite_40 @cite_2 @cite_11 @cite_16 @cite_32 . In @cite_23 , He al observed an interesting phenomenon of haze-free outdoors images that at least one channel has some pixels with very low intensities. Based upon this prior, the transmission and global atmospheric light were roughly estimated. After that, the dehazed image was achieved based on the refined transmission by soft-matting @cite_19 or guided filter @cite_30 as well as the estimated global atmospheric light. In @cite_17 , Berman al proposed a nonlocal image dehazing method, which relied on the assumption that colors of a haze-free image are well approximated by a few hundred distinct colors that form tight clusters in RGB space.
@cite_30 @cite_18 @cite_28 @cite_29 @cite_1 @cite_32 @cite_6 @cite_39 @cite_0 @cite_16 @cite_40 @cite_19 @cite_17 @cite_23 @cite_2 @cite_5 @cite_34 @cite_11
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Most existing image dehazing methods tend to boost local image contrast for regions with heavy haze. Without special treatment, these methods may significantly amplify existing image artifacts such as noise, color aliasing and blocking, which are mostly invisible in the input images but are visually intruding in the results. This is especially the case for low quality cellphone shots or compressed video frames. The recent work of (2014) addresses blocking artifacts for dehazing, but is insufficient to handle other artifacts. In this paper, we propose a new method for reliable suppression of different types of visual artifacts in image and video dehazing. Our method makes contributions in both the haze estimation step and the image recovery step. Firstly, an image-guided, depth-edge-aware smoothing algorithm is proposed to refine the initial atmosphere transmission map generated by local priors. In the image recovery process, we propose Gradient Residual Minimization (GRM) for jointly recovering the haze-free image while explicitly minimizing possible visual artifacts in it. Our evaluation suggests that the proposed method can generate results with much less visual artifacts than previous approaches for lower quality inputs such as compressed video clips. In this paper, we present an end-to-end network, called Cycle-Dehaze, for single image dehazing problem, which does not require pairs of hazy and corresponding ground truth images for training. That is, we train the network by feeding clean and hazy images in an unpaired manner. Moreover, the proposed approach does not rely on estimation of the atmospheric scattering model parameters. Our method enhances CycleGAN formulation by combining cycle-consistency and perceptual losses in order to improve the quality of textural information recovery and generate visually better haze-free images. Typically, deep learning models for dehazing take low resolution images as input and produce low resolution outputs. However, in the NTIRE 2018 challenge on single image dehazing, high resolution images were provided. Therefore, we apply bicubic downscaling. After obtaining low-resolution outputs from the network, we utilize the Laplacian pyramid to upscale the output images to the original resolution. We conduct experiments on NYU-Depth, I-HAZE, and O-HAZE datasets. Extensive experiments demonstrate that the proposed approach improves CycleGAN method both quantitatively and qualitatively. Photographs of hazy scenes typically suffer having low contrast and offer a limited visibility of the scene. This article describes a new method for single-image dehazing that relies on a generic regularity in natural images where pixels of small image patches typically exhibit a 1D distribution in RGB color space, known as color-lines. We derive a local formation model that explains the color-lines in the context of hazy scenes and use it for recovering the scene transmission based on the lines' offset from the origin. The lack of a dominant color-line inside a patch or its lack of consistency with the formation model allows us to identify and avoid false predictions. Thus, unlike existing approaches that follow their assumptions across the entire image, our algorithm validates its hypotheses and obtains more reliable estimates where possible. In addition, we describe a Markov random field model dedicated to producing complete and regularized transmission maps given noisy and scattered estimates. Unlike traditional field models that consist of local coupling, the new model is augmented with long-range connections between pixels of similar attributes. These connections allow our algorithm to properly resolve the transmission in isolated regions where nearby pixels do not offer relevant information. An extensive evaluation of our method over different types of images and its comparison to state-of-the-art methods over established benchmark images show a consistent improvement in the accuracy of the estimated scene transmission and recovered haze-free radiances. We present an approach for easily removing the effects of haze from passively acquired images. Our approach is based on the fact that usually the natural illuminating light scattered by atmospheric particles (airlight) is partially polarized. Optical filtering alone cannot remove the haze effects, except in restricted situations. Our method, however, stems from physics-based analysis that works under a wide range of atmospheric and viewing conditions, even if the polarization is low. The approach does not rely on specific scattering models such as Rayleigh scattering and does not rely on the knowledge of illumination directions. It can be used with as few as two images taken through a polarizer at different orientations. As a byproduct, the method yields a range map of the scene, which enables scene rendering as if imaged from different viewpoints. It also yields information about the atmospheric particles. We present experimental results of complete dehazing of outdoor scenes, in far-from-ideal conditions for polarization filtering. We obtain a great improvement of scene contrast and correction of color. Single image haze removal is an extremely challenging problem due to its inherent ill-posed nature. Several prior-based and learning-based methods have been proposed in the literature to solve this problem and they have achieved superior results. However, most of the existing methods assume constant atmospheric light model and tend to follow a two- step procedure involving prior-based methods for estimating transmission map followed by calculation of dehazed image using the closed form solution. In this paper, we relax the constant atmospheric light assumption and propose a novel unified single image dehazing network that jointly estimates the transmission map and performs dehazing. In other words, our new approach provides an end-to-end learning framework, where the inherent transmission map and dehazed result are learned directly from the loss function. Extensive experiments on synthetic and real datasets with challenging hazy images demonstrate that the proposed method achieves significant improvements over the state-of-the-art methods. 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. This paper focuses on dehazing nighttime images. Most existing dehazing methods use models that are formulated to describe haze in daytime. Daytime models assume a single uniform light color attributed to a light source not directly visible in the scene. Nighttime scenes, however, commonly include visible lights sources with varying colors. These light sources also often introduce noticeable amounts of glow that is not present in daytime haze. To address these effects, we introduce a new nighttime haze model that accounts for the varying light sources and their glow. Our model is a linear combination of three terms: the direct transmission, airlight and glow. The glow term represents light from the light sources that is scattered around before reaching the camera. Based on the model, we propose a framework that first reduces the effect of the glow in the image, resulting in a nighttime image that consists of direct transmission and airlight only. We then compute a spatially varying atmospheric light map that encodes light colors locally. This atmospheric map is used to predict the transmission, which we use to obtain our nighttime scene reflection image. We demonstrate the effectiveness of our nighttime haze model and correction method on a number of examples and compare our results with existing daytime and nighttime dehazing methods' results. Abstract This paper provides a comprehensive survey of methods dealing with visibility enhancement of images taken in hazy or foggy scenes. The survey begins with discussing the optical models of atmospheric scattering media and image formation. This is followed by a survey of existing methods, which are categorized into: multiple image methods, polarizing filter-based methods, methods with known depth, and single-image methods. We also provide a benchmark of a number of well-known single-image methods, based on a recent dataset provided by Fattal (2014) and our newly generated scattering media dataset that contains ground truth images for quantitative evaluation. To our knowledge, this is the first benchmark using numerical metrics to evaluate dehazing techniques. This benchmark allows us to objectively compare the results of existing methods and to better identify the strengths and limitations of each method. In this paper we introduce a novel approach to restore a single image degraded by atmospheric phenomena such as fog or haze. The presented algorithm allows for fast identification of hazy regions of an image, without making use of expensive optimization and refinement procedures. By applying a single per pixel operation on the original image, we produce a 'semi-inverse' of the image. Based on the hue disparity between the original image and its semi-inverse, we are then able to identify hazy regions on a per pixel basis. This enables for a simple estimation of the airlight constant and the transmission map. Our approach is based on an extensive study on a large data set of images, and validated based on a metric that measures the contrast but also the structural changes. The algorithm is straightforward and performs faster than existing strategies while yielding comparative and even better results. We also provide a comparative evaluation against other recent single image dehazing methods, demonstrating the efficiency and utility of our approach. Haze is a major problem in videos captured in outdoors. Unlike single-image dehazing, video-based approaches can take advantage of the abundant information that exists across neighboring frames. In this work, assuming that a scene point yields highly correlated transmission values between adjacent video frames, we develop a deep learning solution for video dehazing, where a CNN is trained end-to-end to learn how to accumulate information across frames for transmission estimation. The estimated transmission map is subsequently used to recover a haze-free frame via atmospheric scattering model. To train this network, we generate a dataset consisted of synthetic hazy and haze-free videos for supervision based on the NYU depth dataset. We show that the features learned from this dataset are capable of removing haze that arises in outdoor scene in a wide range of videos. Extensive experiments demonstrate that the proposed algorithm performs favorably against the state-of-the-art methods on both synthetic and real-world videos. Bad weather, such as fog and haze, can significantly degrade the visibility of a scene. Optically, this is due to the substantial presence of particles in the atmosphere that absorb and scatter light. In computer vision, the absorption and scattering processes are commonly modeled by a linear combination of the direct attenuation and the airlight. Based on this model, a few methods have been proposed, and most of them require multiple input images of a scene, which have either different degrees of polarization or different atmospheric conditions. This requirement is the main drawback of these methods, since in many situations, it is difficult to be fulfilled. To resolve the problem, we introduce an automated method that only requires a single input image. This method is based on two basic observations: first, images with enhanced visibility (or clear-day images) have more contrast than images plagued by bad weather; second, airlight whose variation mainly depends on the distance of objects to the viewer, tends to be smooth. Relying on these two observations, we develop a cost function in the framework of Markov random fields, which can be efficiently optimized by various techniques, such as graph-cuts or belief propagation. The method does not require the geometrical information of the input image, and is applicable for both color and gray images. We present an approach to easily remove the effects of haze from images. It is based on the fact that usually airlight scattered by atmospheric particles is partially polarized. Polarization filtering alone cannot remove the haze effects, except in restricted situations. Our method, however, works under a wide range of atmospheric and viewing conditions. We analyze the image formation process, taking into account polarization effects of atmospheric scattering. We then invert the process to enable the removal of haze from images. The method can be used with as few as two images taken through a polarizer at different orientations. This method works instantly, without relying on changes of weather conditions. We present experimental results of complete dehazing in far from ideal conditions for polarization filtering. We obtain a great improvement of scene contrast and correction of color. As a by product, the method also yields a range (depth) map of the scene, and information about properties of the atmospheric particles. Outdoor images taken in bad weather conditions, such as haze and fog, look faded and have reduced contrast. Recently there has been great success in single image dehazing, i.e., improving the visibility and restoring the colors from a single image. A crucial step in these methods is the calculation of the air-light color, the color of an area of the image with no objects in line-of-sight. We propose a new method for calculating the air-light. The method relies on the haze-lines prior that was recently introduced. This prior is based on the observation that the pixel values of a hazy image can be modeled as lines in RGB space that intersect at the air-light. We use Hough transform in RGB space to vote for the location of the air-light. We evaluate the proposed method on an existing dataset of real world images, as well as some synthetic and other real images. Our method performs on-par with current state-of-the-art techniques and is more computationally efficient. Photographing distant objects is challenging for a number of reasons. Even on a clear day, atmospheric haze often represents the majority of light received by a camera. Unfortunately, dehazing alone cannot create a clean image. The combination of shot noise and quantization noise is exacerbated when the contrast is expanded after haze removal. Dust on the sensor that may be unnoticeable in the original images creates serious artifacts. Multiple images can be averaged to overcome the noise, but the combination of long lenses and small camera motion as well as time varying atmospheric refraction results in large global and local shifts of the images on the sensor. An iconic example of a distant object is Mount Rainier, when viewed from Seattle, which is 90 kilometers away. This paper demonstrates a methodology to pull out a clean image of Mount Rainier from a series of images. Rigid and non-rigid alignment steps brings individual pixels into alignment. A novel local weighted averaging method based on ideas from “lucky imaging” minimizes blur, resampling and alignment errors, as well as effects of sensor dust, to maintain the sharpness of the original pixel grid. Finally, dehazing and contrast expansion results in a sharp clean image. In this paper, we propose a novel learning-based approach for single image dehazing. The proposed approach is mostly inspired by the observation that the color of the objects fades gradually along with the increment of the scene depth. We regard the RGB values of the pixels within the image as the important feature, and use the back propagation neural network to mine the internal link between color and depth from the training samples, which consists of the hazy images and their corresponding ground truth depth map. With the trained neural network, we can easily restore the depth information as well as the scene radiance from the hazy image. Experimental results show that the proposed approach is able to produce a high-quality haze-free image with the single hazy image and achieve the real-time requirement. Photos taken in hazy weather are usually covered with white masks and often lose important details. In this paper, we propose a novel deep learning approach for single image dehazing by learning dark channel and transmission priors. First, we build an energy model for dehazing using dark channel and transmission priors and design an iterative optimization algorithm using proximal operators for these two priors. Second, we unfold the iterative algorithm to be a deep network, dubbed as proximal dehaze-net, by learning the proximal operators using convolutional neural networks. Our network combines the advantages of traditional prior-based dehazing methods and deep learning methods by incorporating haze-related prior learning into deep network. Experiments show that our method achieves state-of-the-art performance for single image dehazing. State-of-the-art single image dehazing algorithms have some challenges to deal with images captured under complex weather conditions because their assumptions usually do not hold in those situations. In this paper, we develop a deep transmission network for robust single image dehazing. This deep transmission network simultaneously copes with three color channels and local patch information to automatically explore and exploit haze-relevant features in a learning framework. We further explore different network structures and parameter settings to achieve tradeoffs between performance and speed, which shows that color channels information is the most useful haze-relevant feature rather than local information. Experiment results demonstrate that the proposed algorithm outperforms state-of-the-art methods on both synthetic and real-world datasets. In this paper, we propose an improved image dehazing algorithm using dark channel prior and Multi-Scale Retinex. Main improvement lies in automatic and fast acquisition of transmission map of the scene. We implement the Multi-scale Retinex algorithm on the luminance component in YCbCr space, obtain a pseudo transmission map whose function is similar to the transmission map in original approach. Combining with the haze image model and the dark channel prior, we can recover a high quality haze-free image. Compared with the original method, our algorithm has two main advantages: (i) no user interaction is needed, and (ii) restoring the image much faster while maintaining comparable dehazing performance.