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This study presents a ZYNQ platform-based image defogging system that improves visibility in foggy conditions. The novel approach refines atmospheric light and transmittance estimation, enhancing image quality for real-time applications.

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Area of Science:

  • Computer Vision
  • Image Processing
  • Embedded Systems

Background:

  • Foggy environments degrade image quality by reducing contrast and losing information.
  • Existing defogging systems often suffer from high costs and poor real-time performance.

Purpose of the Study:

  • To design and implement an efficient, real-time image defogging system on a ZYNQ platform.
  • To overcome limitations of traditional defogging algorithms and improve output image quality.

Main Methods:

  • Developed a sky segmentation algorithm to refine atmospheric light and transmittance estimation.
  • Integrated color balancing post-defogging for enhanced image quality.
  • Utilized the Programmable Logic (PL) capabilities of the ZYNQ platform (FPGA) for parallel processing.

Main Results:

  • The system effectively removes fog, significantly improving image contrast and clarity.
  • Accurate estimation of atmospheric parameters was achieved, minimizing sky region distortion.
  • The implemented system demonstrated real-time processing capabilities.

Conclusions:

  • The ZYNQ-based defogging system offers a cost-effective and real-time solution for foggy image enhancement.
  • The proposed sky segmentation and color balancing methods improve defogging performance.
  • The system meets practical requirements for real-time video processing in adverse weather conditions.