Single Image Haze Removal Using Adaptive Dark Channel Prior And Image Fusion Startegy

Fog removal for degraded image is a fundamental and hot problem in computer vision, which has a wide applicate prospect. However, the reasons for foggy image degradation are very complicated and the information in foggy image itself is insufficient. Currently, the degraded process of a foggy image can not be described by any algorithms and models perfectly, and the previous algorithms are not good enough when using them in particular scene. There is still much works to improve the visual effect. Thus, it is necessary to research the fog-degraded image clearness techniques based on the analysis of image degradation mechanism.As a result objects in images captured under bad weather conditions suffer from low contrast, faint color and shifted luminance. Since the reduction of visibility can dramatically degrade operators’ judgments in work from optical instrument. The dark channel prior has been widely studied for haze removal since it is simple and effective. however, it still suffers from over-saturation, artefacts and dark-look. To resolve these problems, this study proposes a method of single image haze removal using adaptive dark channel and image fusion. The main contributions of this work are as follows: first,according to the transmission local constancy, we use the Weighted normalization algorithm to computer the dark channel of pixel. Secondly, we propose an adaptive modelled by a Gaussian model that produces a more matural recovered image of the sky and other bright regions. Finally, a post fusion method is devised to increase the image information at dense haze region.Experiental results demonstrate that the proposed method significantly improves the visibility of the hazy image than the well-known state-of-the-art approach.