A Fast Method for Haze removal In Remote Sensing Image by Using Guided Filter
Keywords:
Bilateral Filter, Guided Filter, Psnr, Mse and Normalized Cross Correlation.Abstract
The visibility of the open-air images is degraded on account of bad atmosphere conditions. Robust solutions to this problem, a simple and narrative method to eliminate the haze from satellite remote sensing images using Guided filter. The mission is challenging due to variations in dark channel prior, air light, transmission map and radiance map. The presence of haze in the atmosphere degrades the quality of images capture by visible camera sensors. The removal of haze is typically performed under the numerical methods. Due to haziness, an image generally lost color and edges. So dehazing technique restores edge losses and color impacts badly. The method performs a per-pixel treatment, which is straightforward to implement and then apply the guided filter to improve the image quality. Our key perception is that pixels in a given group are regularly non-local, they are spread over the whole image plane and are situated at various separations from the camera. Within the sight of vagueness these fluctuating separations mean distinctive transmission coefficients. Experimental results demonstrate that the proposed method can allow a very fast implementation, it is effective for visual appealing, the quality of reconstructed image is usually specified in terms of Peak Signal to Noise Ratio (PSNR), Mean Square Error (MSE) and normalized cross correlation compared to some state-of-the-art methods.
References
- R Fattal, "Single image dehazing," ACM Transactions on Graphics, vol. 27, no. 3, article 72, pp. 1-9, 2008.
- T M. Bui, H. N. Tran, W. Kim, and S. Kim, "Segmenting dark channel prior in single image dehazing," Electronics Letters, vol. 50, no. 7, pp. 516-518, 2014.
- K He, J. Sun, & X. Tan, "Single image haze removal using dark channel prior," IEEE Transaction on Pattern Analysis and Machine Intelligence, Vol. 33, NO. 12.2011.
- S-C. Huang, J.-H. Ye, and B.-H. Chen, "An advanced single-image visibility restoration algorithm for real-world hazy scenes," IEEE Transactions on Industrial Electronics, vol. 62, no. 5, pp. 2962-2972, 2015.
- G Meng, Y. Wang, J. Duan, S. Xiang, and C. Pan, "Efficient image dehazing with boundary constraint and contextual regularization," in Proceedings of the 14th IEEE International Conference on Computer Vision (ICCV '13), pp. 617-624, IEEE, Sydney, Australia, December 2013.
- S-C. Huang, B.-H. Chen, and W.-J. Wang, "Visibility restoration of single hazy images captured in real-world weather conditions," IEEE Transactions on Circuits and Systems for Video Technology, vol. 24, no. 10, pp. 1814-1824, 2014.
- 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
- D-Y. Zhang, M.-Y. Ju, and X.-M. Wang, "A fast image haze removal algorithm using dark channel prior," Acta Electronica Sinica, vol. 43, no. 7, pp. 1437-1443, 2015.
- C. T. Thurow, Real-Time Image Dehazing, Technical University, Berlin, Germany, 2011.
- B. Cai, X. Xu, K. Jia, C. Qing, and D. Tao, "Dehazenet: An end-to-end system for single image haze removal," IEEE TIP, vol. 25,no.11, pp.5187-5198, 2016.
- K. Tang, J. Yang, and J. Wang, "Investigating haze-relevant features in a learning framework for image dehazing," in CVPR, 2014, pp. 2995- 3000.
- K. He, J. Sun and X. Tang, "Guided image filtering," In Computer Vision - ECCV 2010, vol. 6311, pp. 1-14, 2010.
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