THE METHOD OF IMPROVING FOGGED IMAGES VISIBILITY AND ITS USING IN THE PROCESSING IMAGES COMPUTER SYSTEM

Authors

  • М. Е. Serdiuk Oles Honchar Dnipro National University, Dnipro, Ukraine
  • V. G. Berkut Oles Honchar Dnipro National University, Dnipro, Ukraine
  • S. F. Sirik Oles Honchar Dnipro National University, Dnipro, Ukraine

DOI:

https://doi.org/10.15588/1607-3274-2019-4-16

Keywords:

Image processing, fog and haze removal, fog model, improved visibility, transmission map, atmospheric light evaluation.

Abstract

Context. Presence of fog and haze on digital images may cause problems in processes of recognition, tracking, classification of objects.
Thus methods for removing fog and improving visibility of objects in images obtained under poor visibility conditions are in demand in
many computer vision problems. In foggy weather, contrast and color of an image get worse. Fog removal is often accompanied by artifacts
in the image and color distortion. Therefore, it is relevant to seek methods for correct assessing presence and removal of fog while preserving
image details and colors and developing appropriate methods for blurred images processing.
Objective. The purpose of this research is to find effective approaches to solving the problem of removing fog and haze from digital images
and implementing them in a digital image processing computer system [1].
Method. Main stages of image processing are performed on the intensity channel, which helps to preserve colors. The proposed approach
keeps the values of the processed pixels in an acceptable range, which allows better preservation of image details. Frequency filters
are used to evaluate the transmission map. In a modified method, fog density is estimated using a neural network.
Results. The method of removing fog and haze from single image is proposed. This method effectively improves the objects visibility,
preserves details and colors in the image. A modification of the method with another fog density estimation method is also proposed. The
presented methods were implemented in a computer system [1].
Conclusions. The proposed method and its modification effectively remove fog and haze from single image and improve the objects
distinguishability in them. The implementation of these methods in a computer image processing system [1] has expanded the functionality
of the system and increased its ability to improve the quality of images obtained under poor visibility conditions. The system can be used for
preliminary image processing to prevent errors in further operation of computer vision algorithms.

Author Biographies

М. Е. Serdiuk, Oles Honchar Dnipro National University, Dnipro

PhD, Associate Professor of Computer Technologies Department

V. G. Berkut, Oles Honchar Dnipro National University, Dnipro

Master of Applied Mathematics, graduate student of Applied Mathematics Faculty

S. F. Sirik, Oles Honchar Dnipro National University, Dnipro

Assistant of Computer Technologies Department

References

Serdiuk М. Е., Berkut V. G. Kompiuterna systema lokalizatsii ta vydalennia tinei u tsyfrovykh zobrazhenniakh, Radio Electronics,

Computer Science, Control, 2017, №2(41), pp. 127–133. DOI: 10.15588/1607-3274-2017-2-14

He K., Sun J., Tang X. Single image haze removal using dark channel prior, IEEE Trans. Pattern Anal. Mach. Intell. Dec.

, Vol. 33, No. 12, pp. 2341–2353. DOI: 10.1109/TPAMI.2010.168

Kang Hyun-Jin, Young-Hyung Kim, Yong Hwan Lee Fast Removal of Single Image using Pixel-based Median Channel

Prior, I. J. Advanced Science and Technology Letters, 2015, Vol. 98, pp. 124–127. DOI: 10.14257/astl.2015.98.31

Tan R. T. Visibility in bad weather from a single image, Proceedings. of the 26th IEEE Conference on Computer Vision and

Pattern Recognition (CVPR ‘08). Anchorage, Alaska, 2008, pp. 1–8.

Kim J.-H., Jang W.-D., Sim J.-Y., Kim C.-S. Optimized contrast enhancement for real-time image and video dehazing, Journal

of Visual Communication and Image Representation, 2013, Vol. 24, No. 3, pp. 410–425. DOI: 10.1016/j.jvcir.2013.02.004

Tarel J. P., Hautiere N. Fast Visibility Restoration from a Single Color or Gray Level Image, Proceedings of the 12th IEEE International Conference on Computer Vision. Kyoto, Japan, 2009, pp. 2201–2208. DOI: 10.1109/ICCV.2009.5459251

Fattal R. Dehazing Using Color-Lines, ACM Transactions on Graphics, 2014, Vol. 34, No. 1, P. 13. DOI: 10.1145/2651362

Livingston M. A., Caelan R. Garrett, Zhuming Ai Image Processing for Human Understanding in Low-visibility, Information

Technology Division, Naval Research Laboratory, 2011, pp. 1–9.

Berman D., Treibitz T., Avidan S. Non-Local Image Dehazing, Proceedings of the CVPR Computer Vision and Pattern Recognition. Las Vegas, NV, USA, 2016, pp. 1674–1682. DOI: 10.1109/CVPR.2016.185

Berman D., Treibitz T., Avidan S. Air-light Estimation using Haze-Lines, Proceedings of the IEEE 13th International Conference

on Intelligent Computer Communication and Processing. Stanford, CA, USA, 2017, pp. 5178–5191.

DOI: 10.1109/ICCPHOT.2017.7951489

Narasimhan S. G., Nayar S. K. Removing weather effects from monochrome images, Proceedings of the CVPR Computer Vision

and Pattern Recognition. Kauai, HI, USA, 2001, pp. 186–193. DOI: 10.1109/CVPR.2001.990956

Chen G., Wang T., Zhou H. J. A Novel Physics-based Method for Restoration of Foggy Day Images, Journal of Image and

Graphics, 2008, Vol. 13, No. 5, pp. 888–893. DOI: 10.1109/SNPD.2007.350

He K. M., Sun J., Tang X. O. Guided image filtering, IEEE Transactions on Pattern Analysis and Machine Intelligence,

, Vol. 35, No. 6, pp. 1397–1409. DOI:10.1109/TPAMI.2012.213

Pang J., Au O. C., Guo Z. Improved single image dehazing using guided filter, In Proc. APSIPA ASC. Xi’an, China, 2011,

pp. 1–4.

Zhang Y.-Q., Ding Y., Xiao J.-S., Liu J., Guo Z. Visibility enhancement using an image filtering approach, EURASIP Journal

on Advances in Signal Processing, Vol. 2012, Article 220, 2012, pp. 1– 6. DOI: 10.1186/1687-6180-2012-220

Shi L., Cui Xiao, Yang Li, Gai Zhigang, Chu Shibo, Shi Jing Image Haze Removal Using Dark Channel Prior and Inverse

Image, International Conference on Measurement Instrumentation and Electronics 2016 (ICMIE 2016). Munich, Germany,

, pp. 89–93. DOI: 10.1051/matecconf/20167503008

Huang D., Wang W., Lu J., Chen K. Fast Single Image Haze Removal Method based on Atmospheric Scattering Model,

IFAC-PapersOnLine, 2018, Vol. 51, Issue 17, pp. 211–216. DOI: 10.1016/j.ifacol.2018.08.144

Noor A. Ibraheem, Mokhtar M. Hasan, Rafiqul Z. Khan, Pramod K. Mishra Understanding Color Models: A Review, ARPN

Journal of Science and Technology, 2012, Vol. 2, No. 3, pp. 265–275.

Gonsales R., Vuds R. Cifrovaya obrabotka izobrazhenij : per. s angl. Moscow, Texnosfera, 2005, 1072 p.

Kumari A., Sahoo S. K. Real Time Visibility Enhancement for Single Image Haze Removal, Proc. Int. Conf. on Information

Processing (IMCIP), 2015, pp. 501–507. DOI: 10.1016/j.procs.2015.06.057

Esterhuizen D. C# How to: Image Contrast / D. Esterhuizen [Electronic resource]. Access mode:

https://softwarebydefault.com/2013/04/20/image-contrast/

Godard C., Aodha O. M., Brostow G. Unsupervised Monocular Depth Estimation with Left-Right Consistency, [Electronic resource]. Access mode:http://zpascal.net/cvpr2017/Godard_Unsupervised_Monocular_

Depth_CVPR_2017_paper.pdf.

Arora T., Gurpadam B. Singh, Mandeep C. Kaur Evaluation of a New Integrated Fog Removal Algorithm IDCP with CLAHE,

International Journal of Soft Computing and Artificial Intelligence, 2014, Vol. 2, Issue 1, pp. 12–18.

Bai L., Wu Y., Xie J., Wen P. Real Time Image Haze Removal on Multi-core DSP, 2014 Asia-Pacific International Symposium

on Aerospace Technology, APISAT 2014, 2014, pp. 244–252. DOI: 10.1016/j.proeng.2014.12.532

Tarel J.-P., Hautière N., Cord A., Gruyer D., Halmaoui H. Improved Visibility of Road Scene Images under Heterogeneous

Fog, Proceedings of IEEE Intelligent Vehicles Symposium (IV’10). San Diego, CA, USA, June 21–24, 2010.

DOI:10.1109/IVS.2010.5548128

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Published

2019-11-25

How to Cite

Serdiuk М. Е., Berkut, V. G., & Sirik, S. F. (2019). THE METHOD OF IMPROVING FOGGED IMAGES VISIBILITY AND ITS USING IN THE PROCESSING IMAGES COMPUTER SYSTEM. Radio Electronics, Computer Science, Control, (4), 166–176. https://doi.org/10.15588/1607-3274-2019-4-16

Issue

No. 4 (2019): Radio Electronics, Computer Science, Control

Section

Progressive information technologies

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Copyright (c) 2020 М. Е. Serdiuk, V. G. Berkut, S. F. Sirik

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