IJCSN International Journal of Computer Science and Network, Volume 5, Issue 2, April 2016 ISSN (Online) : 2277-5420 www.IJCSN.org Impact Factor: 1.02 302 Survey on Image Fog Reduction Techniques 1 Pramila Singh, 2 Eram Khan , 3 Hema Upreti, 4 Girish Kapse 1,2,3,4 Department of Electronics and Telecommunication, Army Institute of Technology Pune, Maharashtra 411015, India Abstract - Image contrast often significantly suffers from degradation due to haze, fog or mist spread in atmosphere, and adds more atmospheric light that harms the visibility of image. In this paper, various methods for reduction of fog have been analyzed and compared. The methods described in this paper are immune to the bad weather conditions including haze, fog, mist and other visibility issues caused by aerosols. Furthermore, the most optimum method is determined for processing RGB images. Keywords – Image Defogging, Albedo, Dark Channel Prior, Transmission Map, Bilateral filtering, CLAHE. 1. Introduction Visibility of images often suffers due to fog, mist, and haze present in atmosphere. However, it plays very important role in day to day life such as in video surveillance, navigation control, satellite imaging like environmental studies, weather studies, web mapping and vehicle driving, railway and road traffic analysis. Images which are captured under foggy or hazy weather contains atmospheric degradation particle, as a result light incident on scene get absorbed and scattered. There are many elements which reflect the incident light, bring downs saturation level. This affects low as well high frequency components of the image. Moreover, this degraded image suffers severe contrast loss, bad visibility, very poor performance. Due to contrast loss image dim especially in distant regions and blurred with surrounding area. In order to get rid of this problem, it is necessary to defog the degraded image [7][8]. Fog formation occurs due to condensation of water vapor into tiny droplets suspended in the air. Water vapor is added to the air in various ways such as wind convergence, water fall, heating of water due to sunlight cause evaporation of water from the surface of oceans, estuary and transpiration from plants and lifting Air Mountain. Produced water vapor begin condensing on dust, ice, salt and other particles which are present in atmosphere, in order to form cloud. Fog forms when a cool, stable air mass is trapped underneath a worm and humid air mass, this process make substantial effect on images and lack visibility and visual vividness in a real time system. In this paper, we explore and compere various technique like soft matting, dark prior channel to reduce foggy effect from the image. 2. Literature Survey Conventional schemes of image capture result in a degraded image in bad weather conditions which is difficult to reconstruct. Haze removal from a single image remains a challenging task as haze is dependent on unknown depth information. Over the years many researchers have attempted to overcome this turmoil. R. Fattal [1] proposed a new method which is able to restore image as well as find a reliable transmission map for additional applications such as image refocusing and neon vision. Based on refined model, image is broken down into segments of constant albedo. It is assumed that surface shading and medium transmission are statistically uncorrelated. It uses a single input image. Results are physically sound and produce good result, although it cannot handle heavy images. Also it fails in case the assumption of surface shading and medium transmission being statistically uncorrelated is not met. Tan’s [2] method observed that haze free image must have higher contrast compared to input image. It maximizes local contrast. Dark channel prior used in this method. Atmospheric light is estimated from sky region. Transmission is estimated from coarse map by redefining fine map. Two simple filters are combined on basis of local pixel information therefore computation cost is