You are provided with the abstract of a scientific paper and the abstracts of the papers it references. Your task is to write a Related Work section for the main paper. This section should concisely summarize and integrate the key points from the cited abstracts, reflecting how they relate to the main paper's contributions and context. In your summary, use numerical citation markers like [1], [2], etc., to refer to the corresponding papers.

Main Abstract:
This work addresses the problem of semantic scene understanding under fog. Although marked progress has been made in semantic scene understanding, it is mainly concentrated on clear-weather scenes. Extending semantic segmentation methods to adverse weather conditions such as fog is crucial for outdoor applications. In this paper, we propose a novel method, named Curriculum Model Adaptation (CMAda), which gradually adapts a semantic segmentation model from light synthetic fog to dense real fog in multiple steps, using both labeled synthetic foggy data and unlabeled real foggy data. The method is based on the fact that the results of semantic segmentation in moderately adverse conditions (light fog) can be bootstrapped to solve the same problem in highly adverse conditions (dense fog). CMAda is extensible to other adverse conditions and provides a new paradigm for learning with synthetic data and unlabeled real data. In addition, we present three other main stand-alone contributions: 1) a novel method to add synthetic fog to real, clear-weather scenes using semantic input; 2) a new fog density estimator; 3) a novel fog densification method to densify the fog in real foggy scenes without using depth; and 4) the Foggy Zurich dataset comprising 3808 real foggy images, with pixel-level semantic annotations for 40 images under dense fog. Our experiments show that 1) our fog simulation and fog density estimator outperform their state-of-the-art counterparts with respect to the task of semantic foggy scene understanding (SFSU); 2) CMAda improves the performance of state-of-the-art models for SFSU significantly, benefiting both from our synthetic and real foggy data. The datasets and code are available at the project website.

Cited Abstract(s):
[1]: Images of outdoor scenes captured in bad weather suffer from poor contrast. Under bad weather conditions, the light reaching a camera is severely scattered by the atmosphere. The resulting decay in contrast varies across the scene and is exponential in the depths of scene points. Therefore, traditional space invariant image processing techniques are not sufficient to remove weather effects from images. We present a physics-based model that describes the appearances of scenes in uniform bad weather conditions. Changes in intensities of scene points under different weather conditions provide simple constraints to detect depth discontinuities in the scene and also to compute scene structure. Then, a fast algorithm to restore scene contrast is presented. In contrast to previous techniques, our weather removal algorithm does not require any a priori scene structure, distributions of scene reflectances, or detailed knowledge about the particular weather condition. All the methods described in this paper are effective under a wide range of weather conditions including haze, mist, fog, and conditions arising due to other aerosols. Further, our methods can be applied to gray scale, RGB color, multispectral and even IR images. We also extend our techniques to restore contrast of scenes with moving objects, captured using a video camera.

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[3]: One source of accidents when driving a vehicle is the presence of fog. Fog fades the colors and reduces the contrasts in the scene with respect to their distances from the driver. Various camera-based Advanced Driver Assistance Systems (ADAS) can be improved if efficient algorithms are designed for visibility enhancement in road images. The visibility enhancement algorithm proposed in [1] is not optimized for road images. In this paper, we reformulate the problem as the inference of the local atmospheric veil from constraints. The algorithm in [1] thus becomes a particular case. From this new derivation, we propose to better handle road images by introducing an extra constraint taking into account that a large part of the image can be assumed to be a planar road. The advantages of the proposed local algorithm are the speed, the possibility to handle both color and gray-level images, and the small number of parameters. A new scheme is proposed for rating visibility enhancement algorithms based on the addition of several types of generated fog on synthetic and camera images. A comparative study and quantitative evaluation with other state-of-the-art algorithms is thus proposed. This evaluation demonstrates that the new algorithm produces better results with homogeneous fog and that it is able to deal better with the presence of heterogeneous fog. Finally, we also propose a model allowing to evaluate the potential safety benefit of an ADAS based on the display of defogged images.

[4]: Haze is one of the major factors that degrade outdoor images. Removing haze from a single image is known to be severely ill-posed, and assumptions made in previous methods do not hold in many situations. In this paper, we systematically investigate different haze-relevant features in a learning framework to identify the best feature combination for image dehazing. We show that the dark-channel feature is the most informative one for this task, which confirms the observation of [8] from a learning perspective, while other haze-relevant features also contribute significantly in a complementary way. We also find that surprisingly, the synthetic hazy image patches we use for feature investigation serve well as training data for realworld images, which allows us to train specific models for specific applications. Experiment results demonstrate that the proposed algorithm outperforms state-of-the-art methods on both synthetic and real-world datasets.

[5]: Video and images acquired by a visual system are seriously degraded under hazy and foggy weather, which will affect the detection, tracking, and recognition of targets. Thus, restoring the true scene from such a foggy video or image is of significance. The main goal of this paper was to summarize current video and image defogging algorithms. We first presented a review of the detection and classification method of a foggy image. Then, we summarized existing image defogging algorithms, including image restoration algorithms, image contrast enhancement algorithms, and fusion-based defogging algorithms. We also presented current video defogging algorithms. We summarized objective image quality assessment methods that have been widely used for the comparison of different defogging algorithms, followed by an experimental comparison of various classical image defogging algorithms. Finally, we presented the problems of video and image defogging which need to be further studied. The code of all algorithms will be available at http: www.yongxu.org lunwen.html .

[6]: 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.

[7]: 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.

[8]: 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.

[9]: 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.

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[12]: The images captured in fog conditions have degraded contrast, which makes current image processing applications sensitive and error prone. We propose in this paper an efficient single-image enhancement algorithm suitable for daytime fog conditions and, based on an original mathematical model, for computing the atmospheric veil, taking into account the variation in fog density to the distance. This model is inspired by the functions that appear in partition of unity in the differential geometry field. When observing images captured in fog conditions, usually the fog has a very low density in front of the camera and this density has a nonlinear increase with the distance, such that objects are no longer visible at greater distances. By using our mathematical model, we are able to obtain superior reconstructions of the original fog-free image when compared with traditional methods. Another advantage of our method is the ability to adapt the model in accordance to the density of the fog. A quantitative and qualitative evaluation is performed on both synthetic and real camera images. This evaluation proves that our mathematical model is more suitable for contrast restoration in both homogeneous and heterogeneous fog conditions. Our algorithm is able to perform contrast restoration in real time for both color and grayscale images.

[13]: 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.

[14]: 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.
Fog fades the color of observed objects and reduces their contrast. Extensive research has been conducted on image defogging (dehazing) to increase the visibility of foggy scenes [1] [9] [10] [11]5 [2] [11] [7] . Certain works focus particularly on enhancing foggy road scenes [3] [12] . Recent approaches also rely on trainable architectures [4] , which have evolved to end-to-end models [14] [8] . For a comprehensive overview of defogging dehazing algorithms, we point the reader to [5] [6] . Our work is complementary and focuses on semantic foggy scene understanding. This work investigates the usefulness of image dehazing for semantic scene understanding under fog.