Journal of the Korea Institute of Information and Communication Engineering (한국정보통신학회논문지)

The Korea Institute of Information and Commucation Engineering (한국정보통신학회)
권호 표지
DOI QR코드

DOI QR Code

Local Dehazing Method using a Haziness Degree Evaluator

흐릿함 농도 평가기를 이용한 국부적 안개 제거 방법

  • Lee, Seungmin (Department of Electronic Engineering, Dong-A University) ;
  • Kang, Bongsoon (Department of Electronic Engineering, Dong-A University)
  • Received : 2022.08.19
  • Accepted : 2022.08.27
  • Published : 2022.10.31
Download PDF
⟨ Previous Next ⟩

Abstract

Haze is a local weather phenomenon in which very small droplets float in the atmosphere, and the amount and characteristics of haze may vary depending on the region. In particular, these haze reduce visibility, which can cause air traffic interference and vehicle traffic accidents, and degrade the quality of security CCTVs and so on. Therefore, in the past 10 years, research on haze removal has been actively conducted to reduce damage caused by haze. In this study, local haze removal is performed by weight generation using a haziness degree evaluator to adaptively respond to haze-free, homogeneous haze, and non-homogeneous haze cases. And the proposed method improves the limitations of the existing static haze removal method, which assumes that there is haze in the input image and removes the haze. We also demonstrate the superiority of the proposed method through quantitative and qualitative performance evaluations with benchmark algorithms.

안개는 매우 작은 물방울이 대기 중에 떠돌아다니는 국지적인 기상현상으로 지역에 따라 안개 양과 특성이 다를 수도 있다. 특히 이러한 안개로 인해 가시거리가 줄어들어 항공 교통 방해와 차량 교통사고를 유발할 수 있으며, 보안용 CCTV 등 의 화질을 저하시킨다. 따라서 최근 10년간 안개로 인한 피해를 줄이기 위해 안개제거 연구가 활발히 진행되고 있다. 본 연구에서는 안개가 없을 경우, 안개가 고르게 분포한 경우, 그리고 안개가 국지적으로 다른 경우에 적응적으로 대응할 수 있도록 흐릿함 농도 평가기를 이용한 가중치 생성을 통해 국부적인 안개 제거를 수행한다. 그리고 입력 영상에 안개가 있다고 가정하고 안개를 제거하는 기존의 정적인 방식의 안개제거 방법의 한계점을 개선시킨다. 또한 벤치마크 알고리즘과의 정량 및 정성적 성능 평가를 통해 제안하는 방법의 우수성을 증명한다.

Keywords

Acknowledgement

This paper was supported by research funds from Dong-A University.

References

  1. K. He, J. Sun, and X. Tang, "Single Image Haze Removal Using Dark Channel Prior," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 33, no. 12, pp. 2341-2353, Dec. 2011. https://doi.org/10.1109/TPAMI.2010.168
  2. A. Levin, D. Lischinski, and Y. Weiss, "A Closed-Form Solution to Natural Image Matting," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 30, no. 2, pp. 228-242, Dec. 2007.
  3. Q. Zhu, J. Mai, and L. Shao, "A Fast Single Image Haze Removal Algorithm Using Color Attenuation Prior," IEEE Transactions on Image Processing, vol. 24, no. 11, pp. 3522-3533, Jun. 2015. https://doi.org/10.1109/TIP.2015.2446191
  4. D. Ngo, G. D. Lee, and B. Kang, "Improved Color Attenuation Prior for Single-Image Haze Removal," Applied Sciences, vol. 9, no. 19, p. 4011, Sep. 2019. https://doi.org/10.3390/app9194011
  5. B. Cai, X. Xu, K. Jia, C. Qing, and D. Tao, "DehazeNet: An End-to-End System for Single Image Haze Removal," IEEE Transactions on Image Processing, vol. 25, no. 11, pp. 5187-5198, Aug. 2016. https://doi.org/10.1109/TIP.2016.2598681
  6. G. -J. Kim, S. Lee, and B. Kang, "Single Image Haze Removal Using Hazy Particle Maps," IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences, vol. E101-A, pp. 1999-2002, Nov. 2018. https://doi.org/10.1587/transfun.E101.A.1999
  7. S. Lee, D. Ngo, and B. Kang, "Design of an FPGA-Based High-Quality Real-Time Autonomous Dehazing System," Remote Sensing, vol. 14, p. 1852, Apr. 2022. https://doi.org/10.3390/rs14081852
  8. D. Ngo, S. Lee, Q. -H. Nguyen, T. M . Ngo, G. -D. Lee, and B. Kang, "Simgle Image Haze Removal from Image Enhancement Perspective for Real-Time Vision-Based Systems," Sensors, vol. 20, no. 18, p. 5170, Sep. 2020. https://doi.org/10.3390/s20185170
  9. B. Li, W. Ren, D. Fu, D. Tao, D. Feng, W. Zeng, and Z. Wang, "Benchmarking Single-Image Dehazing and Beyond," IEEE Transactions on Image Processing, vol. 28, no. 1, pp. 492-505, Aug. 2019. https://doi.org/10.1109/TIP.2018.2867951
  10. D. Ngo, G. -D. Lee, and B. Kang, "Haziness Degree Evaluator: A Knowledge-Driven Approach for Haze Density Estimation," Sensors, vol. 21, no. 11, p. 3896, Jun. 2021. https://doi.org/10.3390/s21113896
  11. Z. Lee and S. Shang, "Visibility: How Applicable is the Century-Old Koschmieder Model?," Journal of the Atmospheric Sciences, vol. 73, no. 11, pp. 4573-4581, Nov. 2016. https://doi.org/10.1175/JAS-D-16-0102.1
  12. H. Cho, G. -J. Kim, K. Jang, S. Lee, and B. Kang, "Color Image Enhancement Based on Adaptive Nonlinear Curves of Luminance Features," Journal of Semiconductor Technology and Science, vol. 15, no. 1, pp. 60-67, Feb. 2015. https://doi.org/10.5573/JSTS.2015.15.1.060
  13. C. Ancuti, C. O. Ancuti, R. Timofte, and C. D. Vleeschouwer, "I-HAZE: A Dehazing Benchmark with Real Hazy and Haze-Free Indoor Images," in Proceedings of Advanced Concepts for Intelligent Vision Systems, Poitiers, France, vol. 11182, pp. 620-631, Apr. 2018.
  14. D. Ngo, S. Lee, G. -D. Lee, and B. Kang, "Single-Image Visibility Restoration: A Machine Learning Approach and Its 4K-Capable Hardwawre Accelerator," Sensors, vol. 20, no. 20, p. 5795, Oct. 2020. https://doi.org/10.3390/s20205795