Abstract—In this paper, we address the image dehazing problem through a global feature-restoration pipeline. We propose a dark channel prior-based global image dehazing algorithm which captures and restores the true features of pixels within haze-degraded regions by applying scene depth selection and adaptive filtering. Our scheme harnesses haze and depth features intuitively across a given image without the prior scene depth information. This allows our scheme to sustain a high dehazing efficiency across all image regions irrespective of the local depth variations. We prove that haze degradation is linearly correlated with scene depth and based on this nuance, propose a depth selection and cropping scheme, which guides the adaptive filter iteratively across the image. Secondly, we put forward haze relevant image features and highlight the dark-channel prior for image dehazing. We merge the dark channel prior and scene depth-cropping schemes into a unified dehazing pipeline which is capable of sustaining uniform and robust results across all image regions, in real-time. We verify the superiority of the proposed scheme in terms of speed and robustness through computer-based experiments. Finally, we present comparison results with state-of-the-art and further highlight the comparative superiority of our scheme.

Index Terms—Dark channel prior, real-time dehazing, feature restoration, image enhancement.

Prince Owusu-Agyeman and Xie Wei are with School of Automation Science and Engineering, South China University of Technology, Guangzhou, P.R. China. Yao Yeboah is with School of Automation Science and Engineering, Guangdong University of Technology, Guangzhou, P.R. China.

Cite:Prince Owusu-Agyeman, Xie Wei, Yao Yeboah, "Feature-Centric Image Enhancement via Dehazing," International Journal of Computer and Electrical Engineering vol. 10, no. 2, pp. 146-157, 2018.