Degradation in visibility is often introduced to images captured in poor weather conditions, such as fog or haze. In this paper, a fusion-based transmission estimation method is introduced to adaptively combine two different transmission models. Specifically, the new fusion weighting scheme and the atmospheric light computed from the Gaussian-based dark channel method improves the estimation of the locations of the light sources. To reduce the flickering effect introduced during the process of frame-based dehazing, a flicker-free module is formulated to alleviate the impacts. The system assessments show this approach is capable of superior defogging and dehazing performance, compared to the state-of-the-art methods, both quantitatively and qualitatively

Foggy and hazy weather conditions considerably effect visibility distance which impacts speed, flow of traffic, travel time delay and increases the risk accidents. Bad weather condition is considered a cause of road accidents, since it the poor conditions can effect drivers field of vision. In addition, fog, haze and mist can have negative influences on visual applications in the open air since they decrease visibility by lowering the contrast and whitening the visible color palette. The poor visibility in these images leads to some failures in recognition and detection of the outdoor object systems and also in Intelligent Transportation Systems (ITS). In this thesis, we propose an image visibility restoration algorithm under foggy weather in intelligent transportation systems. Various camera based Advanced Driver Assistant Systems (ADAS), which can be improved by applying the visibility restoration algorithm, have been applied in this field of study to enhance vehicle safety by displaying the image from a frontal camera to driver after visibility enhancement. To remove fog, automatic methods have been proposed which are categorized into two approaches based on the number of input images: 1) methods which are using polarizing filters, 2) methods which are using captured images from different fog densities. In both of these approaches multiple images are required which have to be taken from exactly the same point of view. While these applications can generate good results, their requirements make them impractical, particularly in real time applications, such as intelligent transportation systems. Therefore, in this thesis we introduce a high-performance visibility restoration algorithm only using a single foggy image which applies a recursive filtering to preserve the edge of images and videos in real time and also compute depth map of the scene to restore image. The applied edge preserving filtering is based on a domain transform in which 1-Dimensional edge-preserving filtering is performed by preserving the geodesic distance between points on the curves that is adaptable with wrapping the input signal. The proposed algorithm can be applied in intelligent transportation system applications, such as Advanced Driver Assistance Systems (ADAS). The main features of the proposed algorithm are its speed, which plays a main role in real time applications, since 1-Dimensional operations are used in the applied filtering leads to remarkable speedups in comparison with classical median filters and robust bilateral lfilters. Potential of memory saving is considered as another one advantage of the proposed model and also the parameters of applied edge-preserving filtering do not effect on its computational cost. It is the first edge-preserving filter for color images with arbitrary scales in real time. The proposed algorithm is also able to handle both color and gray-level images and achieves the restored image without the presence of artifacts in comparison with other state-of-the-art algorithms.

In this thesis, a single image dehazing method based on modified dark channel prior and haze (fog) density detection is proposed. Dark channel prior dehazing algorithm is achieved good results for some haze images. However, we observed that haze images contain low and high haze density. Thus, the region of low haze density is unnecessary to dehaze. To solve this problem, we first defined the HSV distance, pixel-based dark channel prior and pixel-based bright channel prior to estimate the haze density. Further to enhance the dehazing performance of dark channel prior, the atmospheric light value and dehazing weighting is revised based on the HSV distance. Then the new transmission map is obtained. After that, a bilateral filter is applied to refine the transmission map, which can provide the higher accuracy of transmission map. Finally, the haze-free image is recovered by combining the input image and the refined transmission map. As a result, high-quality haze-free image can be recovered with lower computational complexity, which can be naturally extended to video dehazing.