In-Depth Analysis of Contemporary Grayscale Image Dehazing Methods: A Comprehensive Review
Main Article Content
Abstract
Since the fog results in loss and color distortion in an image under poor weather circumstances, an efficient image defogging algorithm is required for retrieving various details in an image and hence recovery of a true color information for visibility improvement. In this paper several state-of-art techniques designed by different researchers towards solving this problem in order to enhance the image by improving the existing present day approaches have been presented. Various enhancements in this area are suggested to increase the value of quality parameters which leads to the better defogged image and hence decreases the road accidents existing day by day. Several intelligent techniques including Q-Deep learning methods with other computer vision algorithms have been suggested to improve upon the visibility of the foggy scene. Finally, the whole review have been concluded by pointing out the several advantages and shortcomings of the existing literature.
Downloads
Metrics
Article Details
This work is licensed under a Creative Commons Attribution 4.0 International License.
You are free to:
- Share — copy and redistribute the material in any medium or format for any purpose, even commercially.
- Adapt — remix, transform, and build upon the material for any purpose, even commercially.
- The licensor cannot revoke these freedoms as long as you follow the license terms.
Under the following terms:
- Attribution — You must give appropriate credit , provide a link to the license, and indicate if changes were made . You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
Notices:
You do not have to comply with the license for elements of the material in the public domain or where your use is permitted by an applicable exception or limitation .
No warranties are given. The license may not give you all of the permissions necessary for your intended use. For example, other rights such as publicity, privacy, or moral rights may limit how you use the material.
References
MORTH Homepage, https://morth.nic.in/sites/default/files/Road_Accident.pdf, last accessed 2018/02/15.
Ivana Shopovska et.al.(2018) “Deep Visible and Thermal Image Fusion for Enhanced Pedestrian Visibility.”, Sensors 19(17):3727..
Yu Liu, Xun Chen et.al.,(2017)” Infrared and Visible Image Fusion with Convolutional Neural." International Journal of Wavelets Multiresolution and Information Processing, 16(3).
Yu Han, Yunze Cai,et.al.,(2012)“A New Image Fusion Performance Metric Based on Visual Information Fidelity.” Information Fusion, 14(2).
B. K. Shreyamsha Kumar.(2013) “Image Fusion Based on Pixel Significance Using Cross Bilateral Filter.” Information Fusion ,14(2).
Gaofeng MENG, Ying WANG, Jiangyong DUAN, et.al., (2013)“Efficient Image Dehazing with Boundary Constraint and Contextual Regularization." IEEE International Conference On Computer Vision.
Bolun Cai, Xiangmin Xu, et.al.,(2016) “Dehaze Net: An End-to-End System for Single Image Haze Removal.” IEEE Transactions on Image Processing 25(11).
Adrian Galdran,(2018)“Image Dehazing by Artificial Multiple-Exposure Image Fusion.” Signal Processing149.
Tannistha Pal, Mrinal Kanti Bhowmik, et.al.(2015) “Defogging of Visual Images using SAMEER-TU Database.” Procedia Computer Science46:1676-1683.
Boyi Li, Wenqi Ren,et.al., (2018)“Benchmarking Single Image Dehazing and Beyond.” IEEE Transactions on Image Processing PP(99):
Pallawi, Natarajan, V. (2016) : Enhanced single image uniform and heterogeneous fog removal using guided filter. In Proc. International conference of Artificial Intelligence and Evolutionary Computations in Engineering Systems (ICAIECES). Springer pp. 453-463
Jun, W. L., Rong, Z.: Image Defogging Algorithm of Single Color Image Based on Wavelet Transform and Histogram Equalization, Applied Mathematical Sciences, Vol. 7(79). HIKARI (2013) 3913 – 3921.
. Tarel, J.P., Hautiere, N. (2009) Fast Visibility Restoration from a Single Color or Gray Level Image. In Proc. IEEE 12th International Conference on Computer Vision (ICCV), IEEE 2201-2208.
He, K., Sun, J., Tang, X.(2011) Single image haze removal using dark channel prior, IEEE Trans. On Pattern Analysis and Machine Intelligence, Vol. 33(12). IEEE 2341-2353.
L. K. Choi, J. You, and A. C. Bovik, "LIVE Image Defogging Database," Online: http://live.ece.utexas.edu/research/fog/fade_defade.html, 2015.