APPLICATION OF MACHINE LEARNING BASED RANDOM FOREST REGRESSOR IN IMAGE DEHAZING

Tabrej Khan, Syed Asif Hassan

Abstract


Low visibility causes haze in images due to fog or dust particles in the atmosphere. The haze causes color distortion and even blurring in the images captured. Machine learning approach has been considered to provide optimized haze removal results to generate higher quality images from where information can be extracted. In this context, machine learning-based random forest regressor algorithm along with post-processing techniques was proposed as a superior solution for de-hazing images and thereby generating higher quality images in comparison to other direct de-hazing methods.

Keywords


Machine learning, Dehazing, Random forest Regressor, Haze Removal Algorithms

Full Text:

PDF

References


Matlin, E., & Milanfar, P. (2012, February). Removal of haze and noise from a single image. In Computational Imaging (p. 82960T).

Kotsiantis, S. B., Zaharakis, I., & Pintelas, P. (2007). Supervised machine learning: A review of classification technisques.

He, K., Sun, J., & Tang, X. (2011). Single image haze removal using dark channel prior. IEEE transactions on pattern analysis and machine intelligence, 33(12), 2341-2353.

Caruana, R., & Niculescu-Mizil, A. (2006, June). An empirical comparison of supervised learning algorithms. In Proceedings of the 23rd international conference on Machine learning (pp. 161-168). ACM.

Tan, R. T. (2008, June). Visibility in bad weather from a single image. In Computer Vision and Pattern Recognition, 2008. CVPR 2008. IEEE Conference on (pp. 1-8). IEEE.

Tang, K., Yang, J., & Wang, J. (2014). Investigating haze-relevant features in a learning framework for image dehazing. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 2995-3000).

Xu, H., Guo, J., Liu, Q., & Ye, L. (2012, March). Fast image dehazing using improved dark channel prior. In Information Science and Technology (ICIST), 2012 International Conference on (pp. 663-667). IEEE.

Ancuti, C., & Ancuti, C. O. (2014). Effective contrast-based dehazing for robust image matching. IEEE Geoscience and Remote sensing letters, 11(11), 1871-1875.

Wang, Y., & Wu, B. (2010, October). Improved single image dehazing using dark channel prior. In Intelligent Computing and Intelligent Systems (ICIS), 2010 IEEE International Conference on (Vol. 2, pp. 789-792). IEEE.

Cootes, T. F., Ionita, M. C., Lindner, C., & Sauer, P. (2012, October). Robust and accurate shape model fitting using random forest regression voting. In European Conference on Computer Vision (pp. 278-291). Springer Berlin Heidelberg.

Grömping, U. (2009). Variable importance assessment in regression: linear regression versus random forest. The American Statistician, 63(4), 308-319.

Budagavi, M., Furton, J., Jin, G., Saxena, A., Wilkinson, J., & Dickerson, A. (2015, September). 360 degrees video coding using region adaptive smoothing. In Image Processing (ICIP), 2015 IEEE International Conference on (pp. 750-754). IEEE.

Yeh, C. H., Kang, L. W., Lin, C. Y., & Lin, C. Y. (2012, August). Efficient image/video dehazing through haze density analysis based on pixel-based dark channel prior. In Information Security and Intelligence Control (ISIC), 2012 International Conference on (pp. 238-241). IEEE.

Williams, N., Zander, S., & Armitage, G. (2006). A preliminary performance comparison of five machine learning algorithms for functional IP traffic flow classification. ACM SIGCOMM Computer Communication Review, 36(5), 5-16.

Ma, K., Liu, W., & Wang, Z. (2015, September). Perceptual evaluation of single image dehazing algorithms. In Image Processing (ICIP), 2015 IEEE International Conference on (pp. 3600-3604). IEEE.

Liaw, A., & Wiener, M. (2002). Classification and regression by randomForest. R news, 2(3), 18-22.




DOI: https://doi.org/10.26483/ijarcs.v9i1.5312

Refbacks

  • There are currently no refbacks.


Bookmark and Share


Copyright (c) 2018 International Journal of Advanced Research in Computer Science