Loading [MathJax]/extensions/MathZoom.js
Trends and Prospects of Techniques for Haze Removal From Degraded Images: A Survey | IEEE Journals & Magazine | IEEE Xplore

Trends and Prospects of Techniques for Haze Removal From Degraded Images: A Survey


Abstract:

For the last two decades, image processing techniques have been used frequently in computer vision applications. The most challenging task in image processing is restorin...Show More

Abstract:

For the last two decades, image processing techniques have been used frequently in computer vision applications. The most challenging task in image processing is restoring images that are degraded due to various weather conditions. Mainly, the visibility of outdoor images is corrupted due to adverse atmospheric effects. The visibility of acquired images is reduced in these circumstances. Haze is an atmospheric phenomenon that reduces the clarity of an image. Due to the presence of particles such as dust, dirt, soot, or smoke, there is significant decay in the color and contrast of captured images. Haze present in acquired images causes issues in a variety of computer vision applications. Therefore, enhancing the contrast of a hazy image and restoring the visibility of the scene is essential. Since clear images are required in every application, image dehazing is an important step. Hence, many researchers are working on it. Different methods have been presented in the literature for image dehazing. This study describes various traditional and deep learning techniques of image dehazing from an analytical perspective. The main intention behind this work is to provide an intuitive understanding of the major techniques that have made a relevant contribution to haze removal. In this paper, we have covered different types of contributions toward dehazing based on the traditional method as well as deep learning approaches. With a considerable amount of instinctive simplifications, the reader is expected to have an improved ability to visualize the internal dynamics of these processes.
Page(s): 762 - 782
Date of Publication: 19 May 2022
Electronic ISSN: 2471-285X

Funding Agency:

References is not available for this document.

I. Introduction

Visible images captured in outdoor environments generally have low contrast and faded colors. It is due to the scattering of light that reflects the object’s surface before reaching the camera lens. This scattering is caused due to the presence of various atmospheric particles such as fog, smoke, dust, and soot in the atmosphere. Apart from scattering, the existence of any medium other than air or even changes in air density along the line-of-sight deviates the path of the reflected ray from the scene. Thus, in adverse weather conditions, the irradiance obtained by the camera directly from the scene point gets attenuated. The camera lens also captures the scattered light rays from other objects, called ‘environmental light’ or ‘airlight’ [1]. These phenomena cause decay in color and contrast, lower saturation, and introduce additional noise in the captured image. This type of degradation in visible images is known as the hazing effect. The hazing effect becomes more prominent as the distance between the scene and the camera increases.

Select All
1.
H. Koschmieder, "Theorie der horizontalen sichtweite" in Beitrage zur Physik der Freien Atmosphare, vol. 12, pp. 33-53, 1924.
2.
C. O. Ancuti, C. Ancuti, R. Timofte and C. De Vleeschouwer, "O-HAZE: A dehazing benchmark with real hazy and haze-free outdoor images", Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit. Workshops, pp. 867-8678, 2018.
3.
S. Riaz, M. Anwar, I. Riaz, H.-W. Kim, Y. Nam and M. A. Khan, "Multiscale image dehazing and restoration: An application for visual surveillance", Comput. Mater. Contin, vol. 70, pp. 1-17, 2021.
4.
R. W. Liu, W. X. Y. Chen and Y. Lu, "An enhanced CNN-enabled learning method for promoting ship detection in maritime surveillance system", Ocean Eng., vol. 235, 2021.
5.
Y. Pei, Y. Huang, Q. Zou, Y. Lu and S. Wang, "Does haze removal help CNN-based image classification", Proc. Eur. Conf. Comput. Vis., pp. 682-697, 2018.
6.
Q. Liu, X. Gao, L. He and W. Lu, "Haze removal for a single visible remote sensing image", Signal Process., vol. 137, pp. 33-43, 2017.
7.
X. Li, H. Shen, L. Zhang, H. Zhang, Q. Yuan and G. Yang, "Recovering quantitative remote sensing products contaminated by thick clouds and shadows using multitemporal dictionary learning", IEEE Trans. Geosci. Remote Sens., vol. 52, no. 11, pp. 7086-7098, Nov. 2014.
8.
Y. Meng and H. Wu, "Highway visibility detection method based on surveillance video", Proc. IEEE 4th Int. Conf. Image Vis. Comput., pp. 197-202, 2019.
9.
Z. Cao et al., "Haze removal of railway monitoring images using multi-scale residual network", IEEE Trans. Intell. Transp. Syst., vol. 22, no. 12, pp. 7460-7473, Dec. 2021.
10.
M. Han, Z. Lyu, T. Qiu and M. Xu, "A review on intelligence dehazing and color restoration for underwater images", IEEE Trans. Syst. Man Cybern. Syst., vol. 50, no. 5, pp. 1820-1832, 2020.
11.
H. Fazlali, S. Shirani, M. Bradford and T. Kirubarajan, "Single image rain/snow removal using distortion type information", Multimedia Tools Appl., vol. 81, pp. 1-27, 2022.
12.
Y. Li, S. Y. Michael, S. Brown and R. T. Tan, "Haze visibility enhancement: A survey and quantitative benchmarking", Comput. Vis. Image Understanding, vol. 165, pp. 1-16, 2017.
13.
D. Singh and V. Kumar, "A. comprehensive review of computational dehazing techniques", Arch. Comput. Methods Eng., vol. 26, no. 5, pp. 1395-1413, 2019.
14.
S. Banerjee and S. S. Chaudhuri, "Nighttime image-dehazing: A review and quantitative benchmarking", Arch. Comput. Methods Eng., vol. 28, no. 4, pp. 2943-2975, 2021.
15.
J. Gui, X. Cong, Y. Cao, W. Ren, J. Zhang and D. Tao, "A comprehensive survey on image dehazing based on deep learning", 2021.
16.
H. Ghanbari, M. Mahdianpari, S. Homayouni and F. Mohammadimanesh, "A meta-analysis of convolutional neural networks for remote sensing applications", IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens., vol. 14, pp. 3602-3613, 2021.
17.
M. Kaur, D. Singh, V. Kumar and K. Sun, "Color image dehazing using gradient channel prior and guided l0 filter", Inf. Sci., vol. 521, pp. 326-342, 2020.
18.
G. Sahu, A. Seal, O. Krejcar and A. Yazidi, "Single image dehazing using a new color channel", J. Vis. Commun. Image Representation, vol. 74, 2021.
19.
D. Singh and V. Kumar, "Modified gain intervention filter based dehazing technique", J. Modern Opt., vol. 64, no. 20, pp. 2165-2178, 2017.
20.
A. M. Chaudhry, M. M. Riaz and A. Ghafoor, "A framework for outdoor RGB image enhancement and dehazing", IEEE Geosci. Remote Sens. Lett., vol. 15, no. 6, pp. 932-936, Jun. 2018.
21.
S. Liu et al., "Image de-hazing from the perspective of noise filtering", Comput. Elect. Eng., vol. 62, pp. 345-359, 2017.
22.
C. Sánchez-Ferreira, L. S. Coelho, H. V. H. Ayala, M. C. Q. Farias and C. H. Llanos, "Bio-inspired optimization algorithms for real underwater image restoration", Signal Process. Image Commun., vol. 77, pp. 49-65, 2019.
23.
R. Kapoor, R. Gupta, L. H. Son, R. Kumar and S. Jha, "Fog removal in images using improved dark channel prior and contrast limited adaptive histogram equalization", Multimedia Tools Appl., vol. 78, no. 16, pp. 23 281-23 307, 2019.
24.
Z. Zhu, J. Hu, J. Jiang and X. Zhang, "A hazy image restoration algorithm via JND based histogram equalization and weighted DCP transmission factor", Proc. J. Phys. Conf. Ser., 2021.
25.
G. Sahu and A. Seal, "Image dehazing based on luminance stretching", Proc. Int. Conf. Inf. Technol., pp. 388-393, 2019.
26.
Z. Zhu, H. Wei, G. Hu, Y. Li, G. Qi and N. Mazur, "A novel fast single image dehazing algorithm based on artificial multiexposure image fusion", IEEE Trans. Instrum. Meas., vol. 70, pp. 1-23, 2020.
27.
F.-C. Cheng, C.-C. Cheng, P.-H. Lin and S.-C. Huang, "A hierarchical airlight estimation method for image fog removal", Eng. Appl. Artif. Intell., vol. 43, pp. 27-34, 2015.
28.
B.-H. Chen, S.-C. Huang and J. H. Ye, "Hazy image restoration by bi-histogram modification", ACM Trans. Intell. Syst. Technol., vol. 6, no. 4, pp. 1-17, 2015.
29.
J.-W. Son, H.-J. Kwon, T.-E. Shim, Y.-C. Kim, S.-H. Ahu and K.-I. Sohng, "Fusion method of visible and infrared images in foggy environment", Proc. Int. Conf. Image Process., pp. 433-434, 2015.
30.
Z. Ma, J. Wen, C. Zhang, Q. Liu and D. Yan, "An effective fusion defogging approach for single sea fog image", Neurocomputing, vol. 173, pp. 1257-1267, 2016.

Contact IEEE to Subscribe

References

References is not available for this document.