Image defogging framework using segmentation and the dark channel prior

Sabiha Anan, Mohammad Ibrahim Khan, Mir Md Saki Kowsar, Kaushik Deb*, Pranab Kumar Dhar, Takeshi Koshiba


研究成果: Article査読

8 被引用数 (Scopus)


Foggy images suffer from low contrast and poor visibility problem along with little color information of the scene. It is imperative to remove fog from images as a pre-processing step in computer vision. The Dark Channel Prior (DCP) technique is a very promising defogging technique due to excellent restoring results for images containing no homogeneous region. However, having a large homogeneous region such as sky region, the restored images suffer from color distortion and block effects. Thus, to overcome the limitation of DCP method, we introduce a framework which is based on sky and non-sky region segmentation and restoring sky and non-sky parts separately. Here, isolation of the sky and non-sky part is done by using a binary mask formulated by floodfill algorithm. The foggy sky part is restored by using Contrast Limited Adaptive Histogram Equalization (CLAHE) and non-sky part by modified DCP. The restored parts are blended together for the resultant image. The proposed method is evaluated using both synthetic and real world foggy images against state of the art techniques. The experimental result shows that our proposed method provides better entropy value than other stated techniques along with have better natural visual effects while consuming much lower processing time.

出版ステータスPublished - 2021 3月

ASJC Scopus subject areas

  • 情報システム
  • 数理物理学
  • 物理学および天文学(その他)
  • 電子工学および電気工学


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