A content Aware Method for Single Image Dehazing

In this thesis, we present a content adaptive method for single image dehazing. Since the degradation level affected by haze is related to the depth of the scene and pixels in each specific part of the image (such as trees, buildings or other object) tend to have same depth to the camera, we assume that the degradation level affected by haze of each region is the same. Based on this assumption, we develop a single image dehazing method. irst, we use Dark Channel Prior method to estimate the airlight vector which represents the haze component in hazy images. By the observation that pixels in each specific part of the image (such as trees, buildings or other object) tend to have same depth to the camera, the transmission in this region is also the same. So, we use Mean Shift segmentation to segment our input image into different regions. Then, we recover the scene radiance by using a cost function estimating the transmission in each region. After the transmission map was estimated, we use Soft Matting to refine this transmission map. Results demonstrate the proposed method’s power to remove the haze layer as well as provide a reliable transmission map which can be exploited for further usage.