Multi-Scale Fusion of Enhanced Hazy Images Using Particle Swarm Optimization and Fuzzy Intensification Operators

Padmini T.N, Shankar T


Dehazing from a single image is still a challenging task, where the thickness of the haze depends on depth information. Researchers focus on this area by eliminating haze from the single image by using restoration techniques based on haze image model. Using haze image model, the haze is eliminated by estimating atmospheric light, transmission, and depth. A few researchers have focused on enhancement based methods for eliminating haze from images. Enhancement based dehazing algorithms will lead to saturation of pixels in the enhanced image. This is due to assigning fixed values to the parameters used to enhance an image. Therefore, the enhancement based methods fail in the proper tuning of the parameters. This can be overcome by optimizing the parameters that are used to enhance the images. This paper describes the research work carried to derive two enhanced images from a single input hazy image using particle swarm optimization and fuzzy intensification operators. The two derived images are further fused using multi-scale fusion technique. The objective evaluation shows that the entropy of the haze eliminated images is comparatively better than the state-of-the-art algorithms. Also, the fog density is measured using an evaluator known as fog aware density evaluator (FADE), which considers all the statistical parameters to differentiate a hazy image from a highly visible natural image. Using this evaluator we found that the density of the fog is less in our proposed method when compared with enhancement based algorithms used to eliminate haze from images.

Full Text:



Nayar Shree K. and Srinivasa G. Narasimhan, Vision in bad weather. Computer Vision, the Proceedings of the Seventh IEEE International Conference. Vol. 2, 1999.

S.G. Narasimhan and S. K.Nayar, Contrast restoration of weather degraded images. PAMI. 25: 713-724, 2003.

Schechner Y.Y., Narasimhan S.G., Nayar S.K., Instant dehazing of images using polarization. IEEE Computer Society Conf. on Computer Vision and Pattern Recognition. pp. 325-332, 2001.

Fattal R. Single image dehazing. Int. Conf. on Computer Graphics and Interactive Techniques archive ACM SIGGRAPH. pp. 1-9, 2008.

Tarel, J.P., Hautiere, N. Fast visibility restoration from a single color or gray level image. IEEE Int. Conf. on Computer Vision. pp. 2201-2208, 2009.

He K., Sun J., Tang X. Single image haze removal using dark channel prior. IEEE Int. Conf. on Computer Vision and Pattern Recognition. pp. 1956-1963, 2009.

Padmini, T. N., and T. Shankar. "De-Hazing using Guided and L 0 Gradient Minimization filters." Indian Journal of Science and Technology 9.37, 2016.

Guo, Fan, Hui Peng, and Jin Tang. "Genetic algorithm-based parameter selection approach to single image defogging." Information Processing Letters 116.10: 595-602, 2016.

Zhang, Wenbo, and Xiaorong Hou. "Estimation algorithm of atmospheric light based on ant colony optimization." Proceedings of the 2017 International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence. ACM. 2017.

Tripathi, Abhishek Kumar, and Sudipta Mukhopadhyay. "Removal of fog from images: A review." IETE Technical Review 29.2: 148-156, 2012

Padmini, T. N., and T. Shankar. "A Review on visibility restoration of degraded images under inclement weather conditions.” 2016.

Singh, Dilbag, and Vijay Kumar. "Comprehensive survey on haze removal techniques." Multimedia Tools and Applications 77.8: 9595-9620, 2018.

Codruta Orniana Ancuti and Cosmin Ancuti. Single Image Dehazing by Multi-Scale Fusion. IEEE transactions on image processing. 22(8), 2013.

Braik, Malik, Alaa F. Sheta, and Aladdin Ayesh. "Image Enhancement Using Particle Swarm Optimization." World congress on engineering. Vol. 1. 2007.

Gorai, Apurba, and Ashish Ghosh. "Hue-preserving color image enhancement using particle swarm optimization." Recent Advances in Intelligent Computational Systems (RAICS), IEEE, 2011.

Al-Ameen, Zohair. "Visibility Enhancement for Images Captured in Dusty Weather via Tuned Tri-threshold Fuzzy Intensification Operators." International Journal of Intelligent Systems and Applications 8.8:10, 2016.

Choi, Lark Kwon, Jaehee You, and Alan Conrad Bovik. "Referenceless prediction of perceptual fog density and perceptual image defogging." IEEE Transactions on Image Processing 24.11: 3888-3901, 2015.



  • There are currently no refbacks.

Published by INSIGHT - Indonesian Society for Knowledge and Human Development