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Image Defogging Framework Using Segmentation and the Dark Channel Prior.

Sabiha Anan1, Mohammad Ibrahim Khan1, Mir Md Saki Kowsar1

  • 1Department of Computer Science and Engineering, Chittagong University of Engineering and Technology, Chattogram 4349, Bangladesh.

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|March 3, 2021
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Summary

This study introduces a novel image defogging framework that segments foggy images into sky and non-sky regions. It enhances visibility and color, overcoming limitations of existing Dark Channel Prior methods for clearer, natural-looking results.

Keywords:
dark channel priorfloodfill algorithmimage blendingimage enhancementsegmentation

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Area of Science:

  • Computer Vision
  • Image Processing

Background:

  • Foggy images exhibit low contrast, poor visibility, and color distortion, necessitating effective image defogging techniques.
  • The Dark Channel Prior (DCP) method excels in defogging but struggles with homogeneous regions like skies, causing color distortion and block effects.

Purpose of the Study:

  • To develop an improved image defogging framework that overcomes the limitations of the DCP method, particularly in regions with large homogeneous areas.
  • To enhance the visual quality and naturalness of defogged images by addressing color distortion and block effects.

Main Methods:

  • A novel framework employing sky and non-sky region segmentation using a binary mask generated by the floodfill algorithm.
  • Restoring the foggy sky region using Contrast Limited Adaptive Histogram Equalization (CLAHE) and the non-sky region using a modified Dark Channel Prior (DCP) technique.
  • Blending the restored sky and non-sky regions to produce the final defogged image.

Main Results:

  • The proposed method demonstrates superior performance compared to state-of-the-art techniques on both synthetic and real-world foggy images.
  • Achieved higher entropy values, indicating improved image quality and detail preservation.
  • Produced defogged images with more natural visual effects and significantly reduced processing time.

Conclusions:

  • The proposed segmentation-based defogging framework effectively addresses the limitations of traditional DCP methods, especially in images with sky regions.
  • The combination of CLAHE and modified DCP offers a robust solution for enhancing visibility and color fidelity in foggy conditions.
  • This approach provides a computationally efficient and visually superior alternative for image defogging in computer vision applications.