Analysis of the atmospheric visibility Restoration and fog attenuation using gray scale image

Insufficient visibility in bad climate condition due to haze and fog is a foremost problem in an outdoor image. Thus, fog removal from image and video is required for getting high performance of the vision algorithm. In this thesis, we propose a simple but effective method using dark channel estimation, atmospheric light estimation, transmission estimation, scene radiance recovery to remove haze from images and video. It is based on examination of nearly all local patches in fog-free outdoor images which is having some pixels with very low intensities in at least one color channel. Using this with the fog imaging representation, we can approximate the depth of the fog and able to recover a high quality hazefree image. A transmission map estimated by a guided filter scheme which smooth and respect with depth information of the image. Results reveal that the proposed method achieves high-quality dehazeing effect performance. The proposed method, due to its ability to enhance visibility, may be used with advantages as pre-processing in many systems ranging from surveillance, to remote sensing, intelligent vehicles. Another advantage is the prospect to handle both color images, gray level images and video since the ambiguity between the presence of fog and the objects with low color dispersion is solved by assuming only small objects can have colors with low saturation. This model also allow to evaluate the potential safety advantage of an Advanced driving assistance system (ADAS) based on the display of defogged images.