Fog Removal Research Articles

A Review on Fog Removal with Its Techniques and Types

Now days, Images play essentialrole in real world, images defines not only the changes in environment and also use for traffic analysis. But images are captured in open environment due to the bed whether or atmosphere images are not too good. This paper review on the different haze removal processes to eliminate the haze in the captured hazy images to recover a better and improved quality haze free images [1].

The theoretical analysis of the Fog removal in the LNG Ambient Vaporizer

The fog removal process is one of the important process in LNG Ambient Vaporizer. In this study we carried out theoretical study of the fog removal process in LNG Ambient Vaporizer. The LNG Ambient Vaporizer in Incheon area was used in our study. The fog temperature and the required energy produced from air fan to remove fog in LNG Ambient Vaporizer were calculated using average temperature of Incheon area in 2012 by Psychometruc Chart method. As a result we can be remove fog in LNG Ambient Vaporizer using Enthalpy[kW] energy in summer season and Enthalpy[kW] in winter season respectively.

Improved Haze Removal of Underwater Images using Particle Swarm Optimization

The main objective of fog removal algorithm is to estimate the airlight map for the given image and then perform the necessary operations on the image in order to overcome the fog in the image and enhance the quality of the image. The dark channel prior method of fog removal is more suitable and time-saving in real-time systems. In this paper, an efficient approach for fog removal of foggy images based on the combination of dark channel prior and genetic algorithm is presented. It is found that the proposed method is more suitable for obtaining the better quality of the image than the most of the existing methods.

AN IMPROVED ALGORITHM FOR LOW CONTRAST HAZY IMAGE DETECTION USING DCP

Area of Image Processing includes image quality enhancement, tracking or detecting object, video quality enhancement, video analysis etc. The aim of video analysis is to develop intelligent systems that can take de cisions as per the situations.. Therefore existing work mainly concentrated on video quality enhancement and detect particular object from the video. Following report focuses on those problems, that occurs during bad weather condition (ex. fog, haze, snow or rainy conditions). Existing work shows quality enhancement for low resolution videos or for single images, but few algorithms are developed for enhancing bad weather condition videos, which normally have low resolution, high noise and compression artifacts. To remove the fog fro m the sequence of frames is one of the most challenging task of video processing. Proposed method works with DCP method for i mprove the performance of fog removal process. The propose method first recognize wather input image is a foggy image or low contrast image by CLAHE algorithm and then DCP method apply if input image is foggy image.

A Review on Fog Removal with Its Techniques and Types

A Review on Fog Removal with Its Techniques and Types

A hierarchical airlight estimation method for image fog removal

Fog phenomena result in airlight generation and degrade the visibility of the color image captured from the camera. To improve visibility, airlight estimation is necessary for image fog removal. As airlight is very bright, the traditional methods directly select bright pixels for airlight estimation. However, some bright pixels generated by light sources, such as train headlights, may interfere with the accuracy of the above-mentioned methods. In this paper, we propose a new airlight estimation method. Based on Gaussian distribution, the proposed method selects the airlight candidates in the brightest region of the input image. Moreover, the color similarity estimation is also applied to hierarchically refine the candidates. We then compute the average color from the refined candidate pixels for airlight estimation. Experimental results demonstrate that the proposed method is more accurate than other airlight estimation methods and has low time complexity.

Single image fog removal based on local extrema

Atmospheric conditions induced by suspended particles, such as fog and haze, severely alter the scene appearance. In this paper, we propose a novel defogging method based on the local extrema, aiming at improving the image visibility under foggy or hazy weather condition. The proposed method utilizes atmospheric scattering model to realize the fog removal. It applies the local extrema method to figure out three pyramid levels to estimate atmospheric veil, and manipulates the tone and contrast of details at different scales through multi-scale tone manipulation algorithm. The results on the experiments of comparison with traditional methods demonstrate that the proposed method can achieve more accurate restoration for the color and details, resulting in a great improvement in image visibility.

Adaptive image fog removal based on multi-scale WLS filter

Atmospheric conditions induced by suspended particles, such as fog and haze, severely alter the scene appearance. In this paper, we proposed a novel adaptive defogging method based on the weighted least squares WLS framework, aiming at improving the image visibility under foggy or hazy weather condition. Through the inference of halo intensity and smooth degree, our method adopts image fusion technology based on multi-scale WLS filter to estimate atmospheric veil, and restores the foggy image by inverting the atmospheric scattering model. The results on the experiments of comparison with traditional algorithms demonstrate that the proposed method can achieve more accurate restoration, resulting in a great improvement in image visibility.

Combating Bad Weather Part II: Fog Removal from Image and Video

Every year lives and properties are lost in road accidents. About one-fourth of these accidents are due to low vision in foggy weather. At present, there is no algorithm that is specifically designed for the removal of fog from videos. Application of a single-image fog removal algorithm over each video frame is a time-consuming and costly affair. It is demonstrated that with the intelligent use of temporal redundancy, fog removal algorithms designed for a single image can be extended to the real-time video application. Results confirm that the presented framework used for the extension of the fog removal algorithms for images to videos can reduce the complexity to a great extent with no loss of perceptual quality. This paves the way for the real-life application of the video fog removal algorithm. In order to remove fog, an efficient fog removal algorithm using anisotropic diffusion is developed. The presented fog removal algorithm uses new dark channel assumption and anisotropic diffusion for the initialization and refinement of the airlight map, respectively. Use of anisotropic diffusion helps to estimate the better airlight map estimation. The said fog removal algorithm requires a single image captured by uncalibrated camera system. The anisotropic diffusion-based fog removal algorithm can be applied in both RGB and HSI color space. This book shows that the use of HSI color space reduces the complexity further. The said fog removal algorithm requires pre- and post-processing steps for the better restoration of the foggy image. These pre- and post-processing steps have either data-driven or constant parameters that avoid the user intervention. Presented fog removal algorithm is independent of the intensity of the fog, thus even in the case of the heavy fog presented algorithm performs well. Qualitative and quantitative results confirm that the presented fog removal algorithm outperformed previous algorithms in terms of perceptual quality, color fidelity and execution time. The work presented in this book can find wide application in entertainment industries, transportation, tracking and consumer electronics. Table of Contents: Acknowledgments / Introduction / Analysis of Fog / Dataset and Performance Metrics / Important Fog Removal Algorithms / Single-Image Fog Removal Using an Anisotropic Diffusion / Video Fog Removal Framework Using an Uncalibrated Single Camera System / Conclusions and Future Directions / Bibliography / Authors' Biographies

Direct Growth of Ultrafast Transparent Single-Layer Graphene Defoggers

The idea flat surface, superb thermal conductivity and excellent optical transmittance of single-layer graphene promise tremendous potential for graphene as a material for transparent defoggers. However, the resistance of defoggers made from conventional transferred graphene increases sharply once both sides of the film are covered by water molecules which, in turn, leads to a temperature drop that is inefficient for fog removal. Here, the direct growth of large-area and continuous graphene films on quartz is reported, and the first practical single-layer graphene defogger is fabricated. The advantages of this single-layer graphene defogger lie in its ultrafast defogging time for relatively low input voltages and excellent defogging robustness. It can completely remove fog within 6 s when supplied a safe voltage of 32 V. No visible changes in the full defogging time after 50 defogging cycles are observed. This outstanding performance is attributed to the strong interaction forces between the graphene films and the substrates, which prevents the permeation of water molecules. These directly grown transparent graphene defoggers are expected to have excellent prospects in various applications such as anti-fog glasses, auto window and mirror defogging.

A Novel Defogging Technique for Dehazing Images

Fog is a combination of two components airlight and direct attenuation, degrades the picture quality and creates a lot of problem in video surveillance, tracking and navigation. Thus, to remove it from an image, various defogging methods have been proposed in literature. Defogging can achieved using multiple images and single image fog removal method. One of the prominent methods in literature for defogging is Dark Channel Prior (DCP). This method though quite effective in removing fog from images has very high time complexity. In addition, it does not preserve edges and has halo effect. Therefore, this paper proposes a new technique which overcomes the disadvantage of DCP and at the same time preserves the picture quality. The proposed method is implemented in MATLAB-09 and the simulation results show the proposed method is quite good.

Evaluation of a New Integrated Fog Removal Algorithm IDCP with Airlight

This paper has proposed a new fog removal technique IDCP which will integrate dark channel prior with CLAHE and adaptive gamma correction to remove the fog from digital images. Fog in image reduces the visibility of the digital images. Poor visibility not only degrades the perceptual image quality but it also affects the performance of computer vision algorithms such as object detection, tracking, surveillance and segmentation.Various factors such as fog, mist and haze caused by the water droplets present in the air during bad weather leads to poor visibility. The proposed algorithm is designed and implemented in MATLAB using image processing toolbox. The comparison among Air-light and the proposed algorithm is also drawn based upon certain performance parameters. The comparison analysis has shown that the proposed algorithm has shown quite effective results. Index terms—Visibility restoration, Air-light map, Fog removal and Haze.

An analysis and method for contrast enhancement turbulence mitigation.

A common problem for imaging in the atmosphere is fog and atmospheric turbulence. Over the years, many researchers have provided insight into the physics of either the fog or turbulence but not both. Most recently, researchers have proposed methods to remove fog in images fast enough for real-time processing. Additionally, methods have been proposed by other researchers that address the atmospheric turbulence problem. In this paper, we provide an analysis that incorporates both physics models: 1) fog and 2) turbulence. We observe how contrast enhancements (fog removal) can affect image alignment and image averaging. We present in this paper, a new joint contrast enhancement and turbulence mitigation (CETM) method that utilizes estimations from the contrast enhancement algorithm to improve the turbulence removal algorithm. We provide a new turbulent mitigation object metric that measures temporal consistency. Finally, we design the CETM to be efficient such that it can operate in fractions of a second for near real-time applications.

Image Haze Removal Based Dark Channel Prior

This paper describes the image based on dark channel prior to fog. Dark colors from the statistical laws of outdoor priori no fog image database, it was such a key observation was based on the fact that - the vast majority of each local area outdoor image without fog are present strength of certain of at least one color channel low value pixels. Using this model building, directly estimate the concentration of fog and mist removal recover high quality interference image. Experiments show that the dark channel prior to remove the image haze becomes simple and effective.

電子写真の画像形成について（第2報）キャリア・ブラッシュによる減力並びに"かぶり"除去法

電子写真の画像形成について（第2報）キャリア・ブラッシュによる減力並びに"かぶり"除去法

Haze Removal for a Single Image Using Adaptive Template Dark Channel Prior

In this paper, we propose an adaptive template method based on the dark channel prior. The method combines with the haze imaging model to haze removal for a single image. This method can effectively remove haze from a single input image. According to the characteristics of the image itself and the haze removal effect of the different template we divide the input image into flat region, edge region and texture region. Then, select the lager size template dispose the flat region and use midrange or minitype template dispose the edge region and texture area. Experimental results demonstrate that the proposed algorithm has very good performance for fog removal and retains the image details more effectively.

Fast single-image dehazing method for visible-light systems

Optical imaging systems are often degraded by scattering due to atmospheric particles such as haze, fog, and mist. According to the physical model of atmospheric scattering, two major factors that affect the effect of fog removal are discussed. By using joint probability density definition and the proposed joint mean shift filtering algorithm, discontinuity preserving smoothing and image segmentation are addressed in the sequel to refine the atmospheric veil and estimate the atmospheric light, which overcomes the inherent limitations of the state-of-the-art methods. Simulation results on a variety of outdoor foggy images demonstrate that the proposed method achieves short computation time and good restoration for visibility and color fidelity. An embedded vision enhanced system based on a DSP is designed and runs the proposed algorithm at 15 fps for a color frame of 720×576 pixels .

A new single-image fog removal algorithm based on physical model

Based on the physical model of atmospheric scattering and the optical reflectance imaging model, three major factors which affect the effect of fog removal are discussed in detail, dark channel phenomenon is explained via the optical model, and an approach for solving the parameter in the atmospheric scattering model is rigorously derived from a new perspective. Using gray-scale opening operation and fast joint bilateral filtering techniques, the proposed algorithm can effectively obtain the global atmospheric light and greatly improve the speed and accuracy of atmospheric scattering function solving. Finally, the scene albedo is recovered by inverting this model. Compared with existing algorithms, complexity of the proposed method is only a linear function of the number of input image pixels and this allows a very fast implementation. The simulation results show that the processing time of images with a resolution of 576*768 is only 1.7s; Results on a variety of outdoor foggy images demonstrate that the proposed method achieves good restoration for contrast and color fidelity, resulting in a great improvement in image visibility.

基于大气散射模型的单幅图像快速去雾

Based on the physical model of atmospheric scattering and an optical reflectance imaging model,three major factors influencing the fog removal for a single image were discussed in detail.The dark channel phenomenon was explained by the optical model,and the method to solve the parameters of atmospheric scattering model was rigorously derived from a new view.The gray-scale opening operation was used to eliminate the interference from a while object to obtain the global atmospheric light and the fast joint bilateral filtering technique was proposed to greatly improve the speed and accuracy of atmospheric scattering function solving.Finally,the scene albedo was recovered by inverting this model.Experiments show that the method can remove effectively the effect of lights from sky and environments and can recover the color and definition of original scenes.The simulation results indicate that the processing time for an image of 576×768 spends only by 1.7 s.As compared with the existing algorithm,obtained results on a variety of outdoor foggy images demonstrate that the proposed method achieves good restoration for contrast and color deity,and improves image visibility greatly.

Experimental Studies on Corona Discharge Ionization

This study investigates the possibility of corona discharge ionization technique for reduction of fog. For corona discharge ionization a high voltage power supply is designed and developed indigenously. The high output voltage is developed by using isolation transformer, autotransformer, and high voltage neon transformer and it is converted into DC voltage by using Wheatstone bridge circuit and high voltage capacitor. Formation of negative corona, positive corona and voltage-current characteristics of corona ionization was studied. By selecting -9 kV voltages to discharge electrode, the performance of this circuit was tested for removal of fog, Fog was generated by using ultrasonic water fogger collected in closed glass container. Various runs were carried out. The light intensity was measured for each run as a function of time, in presence and absence of the discharge. After continuous operation of device for 6 minutes, 90% to 95% of the aerosols were removed from container in addition to the natural decay effect.