Balkan Journal of Electrical and Computer Engineering

2147-284X 2147-284X

Balkan Yayın

10.17694/bajece.1127633

Electrical Engineering

Elektrik Mühendisliği

Single Image Dehazing based on Additive Wavelet Transform

https://orcid.org/0000-0002-4740-3259

Kaplan

Nur Hüseyin

Erzurum Teknik Üniversitesi

01 30 2023

11 1 71 77 06 08 2022 12 16 2022

2013

Balkan Journal of Electrical and Computer Engineering

In this work, a single image dehazing method, which uses the multiscale products of additive wavelet transform as a prior is presented. In this method, first, the additive wavelet transform is applied to the hazy image to obtain its approximation and wavelet layers. Then the multiscale products of the approximation and detail layers of the input hazy image is calculated. The multiscale products of approximation and wavelet layers are summed up to obtain the proposed prior. Observations demonstrate that the proposed prior calculation keeps the detail information of the image, while detecting the haze. Using the proposed prior and commonly used hazy image model together, an efficient dehazing method is constructed. The comparisons between the proposed method and commonly used dehazing methods show that the proposed method has a better dehazing perfomrance than the traditional methods.

Dehazing Defogging Additive wavelet transform

[1] T. K. Kim, J. K. Paik, and B. S. Kan, “Contrast enhancement system using spatially adaptive histogram equalization with temporal filtering,” IEEE Transactions on Consumer Electronics, vol. 44, no. 1, pp. 82–87, 1998.

[2] J. A. Stark, “Adaptive image contrast enhancement using generalizations of histogram equalization,” IEEE Transactions on Image Processing, vol. 9, no. 5, pp. 889–896, 2000.

[3] J.-Y. Kim, L.-S. Kim, and S.-H. Hwang, “An advanced contrast enhancement using partially overlapped sub-block histogram equalization,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 11, no. 4, pp. 475–484, 2001.

[4] S. G. Narasimhan and S. K. Nayar, “Contrast restoration of weather degraded images,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 25, no. 6, pp. 713–724, 2003.

[5] S. Shwartz, E. Namer, and Y. Y. Schechner, “Blind haze separation,” in 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’06), vol. 2, 2006, pp. 1984–1991.

[6] J. Kopf, B. Neubert, B. Chen, M. Cohen, D. Cohen-Or, O. Deussen, M. Uyttendaele, and D. Lischinskii, “Deep photo: Model-based photograph enhancement and viewing,” ”ACM Trans. Graph., vol. 27, no. 5, pp. 116:1–116:10, 2008.

[7] R. T. Tan, “Visibility in bad weather from a single image,” in 2008 IEEE Conference on Computer Vision and Pattern Recognition, 2008, pp. 1–8.

[8] 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, 2011.

[9] R. Fattal, “Single image dehazing,” ACM Trans. Graph., vol. 27, no. 3, pp. 72:1–72:9, 2008.

[10] J. P. Tarel and N. Hauti`ere, “Fast visibility restoration from a single color or gray level image,” in 2009 IEEE 12th International Conference on Computer Vision, 2009, pp. 2201–2208.

[11] G. Meng, Y. Wang, J. Duan, S. Xiang, and C. Pan, “Efficient image dehazing with boundary constraint and contextual regularization,” in 2013 IEEE International Conference on Computer Vision, 2013, pp. 617–624.

[12] R. Fattal, “Dehazing using color-lines,” ACM Trans. Graph., vol. 34, no. 1, pp. 13:1–13:14, 2014.

[13] 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, 2015.

[14] I. Riaz, X. Fan, and H. Shin, “Single image dehazing with bright object handling,” IET Computer Vision, vol. 10, no. 8, pp. 817–827, 2016.

[15] 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, 2016.

[16] N. H. Kaplan, K. K. Ayten, and A. Dumlu, “Single image dehazing based on multiscale product prior and application to vision control,” Signal, Image and Video Processing, 2017.

[17] J. Nunez, X. Otazu, O. Fors, A. Prades, V. Pala, and R. Arbiol, “Multiresolution-based image fusion with additive wavelet decomposition,” IEEE Transactions on Geoscience and Remote Sensing, vol. 37, no. 3, pp. 1204–1211, 1999.

[18] K. He, J. Sun, and X. Tang, “Guided image filtering,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 35, no. 6, pp. 1397-1409, 2013.

[19] S. G. Narasimhan and S. K. Nayar, “Vision and the atmosphere,” International Journal of Computer Vision, vol. 48, no. 3, pp. 233–254, 2002.

[20] Y. Chai, H. Li, and M. Guo, “Multifocus image fusion scheme based on features of multiscale products and pcnn in lifting stationary wavelet domain,” Optics Communications, vol. 284, no. 5, pp. 1146 – 1158, 2011.