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      Image Dehazing by Incorporating Markov Random Field with Dark Channel Prior

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          As one of the most simple and effective single image dehazing methods, the dark channel prior (DCP) algorithm has been widely applied. However, the algorithm does not work for pixels similar to airlight ( e.g., snowy ground or a white wall), resulting in underestimation of the transmittance of some local scenes. To address that problem, we propose an image dehazing method by incorporating Markov random field (MRF) with the DCP. The DCP explicitly represents the input image observation in the MRF model obtained by the transmittance map. The key idea is that the sparsely distributed wrongly estimated transmittance can be corrected by properly characterizing the spatial dependencies between the neighboring pixels of the transmittances that are well estimated and those that are wrongly estimated. To that purpose, the energy function of the MRF model is designed. The estimation of the initial transmittance map is pixel-based using the DCP, and the segmentation on the transmittance map is employed to separate the foreground and background, thereby avoiding the block effect and artifacts at the depth discontinuity. Given the limited number of labels obtained by clustering, the smoothing term in the MRF model can properly smooth the transmittance map without an extra refinement filter. Experimental results obtained by using terrestrial and underwater images are given.

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          Author and article information

          Journal of Ocean University of China
          Science Press and Springer (China )
          02 May 2020
          01 June 2020
          : 19
          : 3
          : 551-560
          1College of Information Science and Engineering, Ocean University of China, Qingdao 266100, China
          Author notes
          *Corresponding author: WANG Guoyu, E-mail: gywang@

          # These authors contributed equally to this work.

          Copyright © Ocean University of China, Science Press and Springer-Verlag GmbH Germany 2020.

          The copyright to this article, including any graphic elements therein (e.g. illustrations, charts, moving images), is hereby assigned for good and valuable consideration to the editorial office of Journal of Ocean University of China, Science Press and Springer effective if and when the article is accepted for publication and to the extent assignable if assignability is restricted for by applicable law or regulations (e.g. for U.S. government or crown employees).

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