Survey on Image Fog Reduction Techniques

2017

Images are very important parts of dat to day life. They plays very important role in analyzing traffic on roadways, railways and airways.Sometimes due to bad weather effect the analysis through these images becomes difficult. As weather effect degrade the quality of images and those images suffer low contrast, color alteration and shrink the resolution of the captured object in open-air. The reason behind this problem is that the light capture by the lens of the capturing device get spread by the atmosphere. So it was found that conventional techniques used for enhancing the images are not sufficient for removing foggy effect or any other weather effect from the captured images. In this work, we have analyzed the hand techniques employed for image processing. And through that analysis we propose a technique which is efficient technique for enhancing the quality of degraded images. This technique consists of two phases, the first phase is used to remove fog from an image using a Fog...

Computer vision applications such as Object Detection, Outdoor Surveillance, Object Tracking, Segmentation, consumer electronics and many more require restoration of images captured in foggy environment. Fog/haze is formed as a result of environment attenuation and air light (scattering of light) resulting in image degradation since the contrast of the scene is reduced by attenuation while the whiteness in the scene is increased by airlight. Hence, the objective of fog removal algorithms is to recover the color and contrast of the scene. Also, formation of fog is the function of the depth and estimation of depth information requires assumptions or prior information of the single image. Hence, with various assumptions on the single image, fog removal algorithms estimate the depth information, which are discussed in this paper.

—Haze is framed because of the two major phenomena that are nature constriction and the air light. This paper introduces an audit on the diverse methods to expel fog from pictures caught in murky environment to recuperate a superior and enhanced nature of murkiness free pictures. Pictures of open air scenes regularly contain corruption because of cloudiness, bringing about difference decrease and shading blurring. Haze evacuation overall called perceivability rebuilding alludes to various frameworks that assume to reduce or empty the corruption that have happened while the computerized picture was being gained. This paper is an audit on the different mist evacuation calculations. Cloudiness evacuation techniques recuperate the shading and differentiation of the scene.In this paper, different haze evacuation methods have been examined.

The images captured in fog conditions have degraded contrast, that makes current image processing applications sensitive and error prone. We propose in this paper an efficient image enhancement algorithm suitable for daytime fog conditions and based on the Koschmieder's model. Using this mathematical model together with an original inference of the atmospheric veil induced by the fog we are able to recover the original fog-free image. A quantitative and qualitative evaluation is performed on both synthetic and real camera images. Our algorithm is suitable for both color and gray scale images and is able to perform image enhancement in real time.

Haze removal technique refer to the procedure attempt to remove the haze from a hazy/degraded images effected by bad weather. Many researchers have proposed various method/Algorithms for improvement in the hazed images and get the better results using various restoration techniques. In this paper we have analyze various hazed removal techniques like Dark Chanel Prior method (DCP), Contrast limited adaptive histogram equalization (CLAHE). We also observed that a large research space exist for haze removal by combining nature inspired algorithms like PSO, ABC with the Techniques in vogue. I. INTRODUCTION Most of the time the quality of the outdoor image is degraded due to atmospheric weather condition. Hazed image fig1. (a)[4] is the result of atmospheric absorption and scattering of air light. Such images are captured under the bad visibility or bad weather condition. Haze free image shown in fig1. (b) The degraded images lose contrast due to attenuation and color fidelity due to increase the whiteness (air light) in the scene and Therefore haze removal is a challenging problem because the haze is dependent on the unknown depth information. During the past decade many researcher have explored many methods by using single or multiple images and some more constraints are obtained of multiple images of the same scene under different weather condition. A dehazing method can significantly increase the visibility of the scene and correct the color shift caused by air light which are the phenomena of visibility restoration. A haze free image is more visually pleasing.

International Journal of Advance Research and Innovative Ideas in Education, 2017

One of the key issues within the space of image process is that the restoration of the images those are corrupted as a result of numerous degradations. Images of outside scenes taken during a bad weather conditions comprises of atmospherical degradation. As the light travels from the scene point towards observer, it is scattered and absorbed by particles in space like haze and fog. Attributable to the presence of those atmospherical particles there's a resultant degradation within the color and distinction within the captured image within the bad weather conditions. This causes difficulty in detecting the objects of images. During current days attributable to the recent development of the machine vision area, it's doable to enhance the outside hazy pictures and take away the haze from the images. Images captured in foggy climatic conditions usually has poor visibility, this can produce plenty of impacts on the outside computer vision systems, like video police work, intelligent transportation help system, and remote sensing house cameras then on. In this review paper, we've given and compared a study of varied fog/haze removal algorithms/techniques for image processing. The clear objective of this paper is to explore the pitfalls of the techniques utilized in the revolutionary era of image processing applications.

Fog is the natural phenomenon that causes severe difficulties in driving & results in major accidents. Fog degrades the view of an object and results in poor visibility. The poor visibility of an object becomes challenge to the driver to identify the object and monitor it. It creates lots of difficulties in driving and monitoring the vehicle. There is lots of research on the topic but still the problem has not solved to the desired result. There exist several kinds of environment variations that make the foggy image enhancement more difficult. Therefore an efficient algorithm is required to cope up with several challenges arising from the nature of visibility enhancement of foggy images.

IJEER , 2022

A literature review aids in comprehending and gaining further information about a certain area of a subject. The presence of haze, fog, smoke, rain, and other harsh weather conditions affects outdoor photos. Images taken in unnatural weather have weak contrast and poor colors. This may make detecting objects in the produced hazy pictures difficult. In computer vision, scenes and images taken in a foggy atmosphere suffer from blurring. This work covers a study of many remove haze algorithms for eliminating haze collected in real-world weather scenarios in order to recover haze-free images rapidly and with improved quality. The contrast, viewing range, and color accuracy have been enhanced. All of these techniques it is used in countless fields. Some of the applications that use this technology outdoor surveillance, object recognition, underwater photography, and so on.

Images plays an important role in the real world, images are used for describing the changes in the environment. Images are captured in open environment due to the bad weather or atmosphere images are not a clear. Images acquired in bad weather, such as the fog and haze, are extremely degraded by scattering of an atmosphere, and decreases contrast. The bad weather not only lead to variant of the visual outcome of image, but also to the difficulty of the post processing of the image. Images captured during adverse weather conditions frequently feature degraded visibility and undesirable color cast effects. The presence of suspended particles like haze, fog and mist in the atmosphere deteriorates quality of captured images. In this paper, we have proposed a dark channel prior and contrast limited adaptive histogram equalization technique, it is based on adaptive histogram equalization. The dark channel prior technique is helpful to clear the hazy images. Removing haze effects on image is a challenging and meaningful task for image processing and computer vision applications. In this work we remove haze from hazy image, and improve the quality of an image and then at last we obtain restored enhance haze-free image with clear visibility. The proposed technique is designed and implemented in MATLAB.

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. Citation: Hongyu Zhao, Chuangbai Xiao, Jing Yu, Xiujie Xu. Single image fog removal based on local extrema. IEEE/CAA Journal of Automatica Sinica, 2015, 2(2): 158-165