Visibility Enhancer: Adaptable for Distorted Traffic Scenes by Dusty Weather

Abstract

Poor weather conditions such as the presence of heavy snow, fog, rain and dust storm are considered as dangerous restrictions of the functionality of cameras via reducing clear visibility. Thus, they have bad effect on computer vision algorithms used in traffic scene understanding, such as object detection, tracking, and recognition which are vital for traffic monitoring. Current methods for image enhancement can not be utilized under the influence of weather variability from foggy to dusty situations. This paper proposes an adaptive technique for visibility enhancement based on the bright balance and Laplace filtering. The overall visibility enhancement process is composed of three main parts: color and illumination improvement, reflection and component details enhancement, and linear weighted fusion. First, the contrast of an image is enhanced by auto white balance and Gamma correction for each color channel (Red, Green, Blue) individually to achieve color enhancement and outperform the illumination. Second, the detail enhancement is achieved by the Laplace pyramid filter to process the reflection component. Third, the detail enhanced layer is added back to the corrected color layer to reconstruct the clear image. The quantitative results and visual analysis demonstrate the efficacy of the proposed technique. Comparing with the state-of-the-art image enhancement methods, the evaluation of the objective metrics have shown that the contrast of unclear images can be effectively improved by the proposed method and with well effects on both foggy and dusty situations.