Cuckoo search algorithm-based brightness preserving histogram scheme for low-contrast image enhancement
This paper introduces a novel optimized brightness preserving histogram equalization approach to preserve the mean brightness and to improve the contrast of low-contrast image using cuckoo search algorithm. Traditional histogram equalization scheme induces extreme enhancement and brightness change ensuing abnormal appearance. The proposed method utilizes plateau limits to modify histogram of the image. In this method, histogram is divided into two sub-histograms on which histogram statistics are exploited to obtain the plateau limits. The sub-histograms are equalized and modified based on the calculated plateau limits obtained by cuckoo search optimization technique. To demonstrate the effectiveness of proposed method a comparison of the proposed method with different histogram processing techniques is presented. Proposed method outperforms other state-of-art methods in terms of the objective as well as subjective quality evaluation.
KeywordsCuckoo search algorithm Histogram equalization Low-contrast satellite images Optimized Plateau limit and brightness preservation
The authors wish to thank the editors and anonymous referees for their constructive criticism and valuable suggestions.
Compliance with ethical standards
Conflict of interest
All authors declare that they have no conflict of interest.
This article does not contain any studies with animals performed by any of the authors.
- Bhandari AK, Kumar A, Padhy PK (2011) Enhancement of low contrast satellite images using discrete cosine transform and singular value decomposition. World Acad Sci Eng Technol 79:35–41Google Scholar
- Canny J (1987). A computational approach to edge detection. In: readings in computer vision (pp. 184–203)Google Scholar
- Eramian, M., Mould, D. (2005, May). Histogram equalization using neighborhood metrics. In : IEEE Computer and robot vision, 2005 proceedings. the 2nd Canadian conference on, pp 397–404Google Scholar
- Gonzalez RC, Woods RE, Eddins SL (2009) Digital image processing using MATLAB. Gatesmark Publishing, USAGoogle Scholar
- Image Processing Place (http://www.imageprocessingplace.com/root_files_V3/image_databases.htm)
- kodak lossless true color image suite (http://r0k.us/graphics/kodak/)
- NASA Earth Observatory (http://earthobservatory.nasa.gov/)
- Wang GG, Tan Y (2017) Improving metaheuristic algorithms with information feedback models. IEEE Trans Cybern 99:1–14Google Scholar
- Wang GG, Gandomi AH, Yang XS, Alavi AH (2016b) A new hybrid method based on krill herd and cuckoo search for global optimisation tasks. Int J BioInsp Comput 8(5):286–299Google Scholar
- Wang GG, Cai X, Cui Z, Min G, Chen J (2017) High performance computing for cyber physical social systems by using evolutionary multi-objective optimization algorithm. IEEE Trans Emerg Topics ComputGoogle Scholar
- Yang XS (2010). A new metaheuristic bat-inspired algorithm. In: Nature inspired cooperative strategies for optimization (NICSO 2010) (pp 65–74). Springer, Berlin, HeidelbergGoogle Scholar
- Yang, X. S., Deb, S. (2009, December). Cuckoo search via Lévy flights. In: IEEE Nature and biologically inspired computing, 2009. NaBIC 2009. World Congress on (pp 210–214)Google Scholar