International Research Journal of Engineering and Technology (IRJET) Volume: 04 Issue: 02 | Feb -2017
www.irjet.net
e-ISSN: 2395 -0056 p-ISSN: 2395-0072
High Efficiency Haze Removal Using Contextual Regularization Algorithm Gayathri.T 1, Geethanjali.M 2, Hasika.D 3 ,A.JerrinSimla 4 4A.JerrinSimla
,Professor, Dept. of Computer Science & Engineering, Panimalar Institute Of Technology, Chennai,Tamilnadu, India
---------------------------------------------------------------------***--------------------------------------------------------------------Abstract - The dark channel prior is one of the most outdoor recognition system and intelligent efficient de-hazing techniques in recent years. However, transportation system are badly affected due to fog or it produces annoying halo effects and reduces image haze. Scattering of light is caused by two fundamental quality level. To overcome this drawback, many filter phenomena such as attenuation and air light. By using concepts have been proposed and combined with the fog/haze removal algorithms, we can enhance/ dark channel prior operation. However, these filters increase the stability, efficiency and robustness of the induce enormous computational burden while the devisual system. Haze removal is a difficult task because hazing effect of the dark channel prior still has room for fog depends upon the unknown scene depth improvement. To get filtered image as well as no information may be. Fog/Haze effect is defined as event changes can be occurred in image; a good efficient of distance between camera and object. Hence removal refinement method based on the gain intervention is of fog needs the estimation of air light map or depth proposed and mixed with the dark channel prior to solve map. The haze removal techniques can be classified the above mentioned drawbacks present in it. As into two technologies: image enhancement and image demonstrated in our proposed concept, the proposed restoration techniques. Image enhancement excludes filter integrated into the dark channel prior yields not the reason why fog destroys image quality. This only execution speeds but also superior recovery effects technique improves or enhances the contrast of haze than can existing state of the-art imaging filters. More image but it leads to loss of wanted information importantly, the dark channel prior combined with the presented in image. Image restoration can be defined filter technique possesses the highest potential for as studies the physical procedure of imaging in fog practical application due to its superior haze removal included image. effect and time complexity. Our main aim of fog removal After observing degradation style of fog, image will implementation is to estimate the air light estimation for surely be established. Finally, the aim of the the given image and then perform the necessary degradation process is used to produce the fog free operations on the image in order to overcome the fog/ image. haze in the image and without reducing the quality of the image. 2. LITERATURE SURVEY Key Words: Dehazing, Dark Channel, Filtering, 1. INTRODUCTION Visibility restoration can be defines as different ways that makes to reduce and limit the degradation which occurs when a digital image is taken. The image suffers from distortion and degradation due to reasonable disadvantages such as object-camera motion, blur image occur due to camera missing focus, relative atmospheric turbulence etc. The main reason of image degradation occurs due to bad weather conditions, low vision camera such as haze, fog, snow and rain. During Fog, when we capture an image using a normal or digital vision based camera then the light gets scattered before entering the camera due to some impurities or unwanted content present in the atmosphere. Due to this, automatic camera monitoring system, indoor/ Š 2017, IRJET
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Shriya Sharma and Sakshi Bhalla[1] proposed a method that dark channel prior of input image is evaluated. To get the haze free image, restoration value is implemented. This experimental results that haze free image are processed efficiently fog presented image. Qingsong Zhu Jiaming Mai and Ling Shao [2] proposed a method with the depth map of the hazy image, we estimate the transmission and restore the scene by atmospheric scattering model, and thus remove the fog from input image. Experimental results show that the proposed approach gives state of the haze removal algorithms in terms of efficiency and also dehazing effect. Apurva S. Bhutad& R.R.Deshmukh [3] proposed method that the overall objective of their paper is to implement the various methods for reducing haze functioning removal algorithm.
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