Abstract
Histogram equalization (HE) is a classical enhancement method for image processing. However, conventional HE techniques have poor performance in terms of preserving the brightness and natural appearance of images, meaning they typically fail to produce satisfactory results. A novel two-dimensional HE method with two-level segmentation for refining image brightness is proposed in this paper. Additionally, a modified two-dimensional histogram is generated to determine the locations of main segmentation points based on neighborhood matrices. The weights of the absolute brightness differences between low and high local contrast regions in this two-dimensional histogram are adjustable. After separating images into two main areas based on main segmentation points, multiple sub-segmentation points are selected based on a novel criterion derived from the maximum value distribution of the double histograms. Experimental results for various test images demonstrate that the proposed method achieves excellent performance in terms of brightness preservation and image contrast enhancement.
Similar content being viewed by others
References
Abdullah-Al-Wadud M, Kabir MH, Dewan MAA et al (2003) A dynamic histogram equalization for image contrast enhancement. IEEE Trans Consum Electron 53(2):593–600. https://doi.org/10.1109/tce.2007.381734
Arriagagarcia EF, Sanchezyanez RE, Ruizpinales J et al (2015) Adaptive sigmoid function bi-histogram equalization for image contrast enhancement. J Electron Imaging 24(5):053009. https://doi.org/10.1117/1.jei.24.5.053009
Berkerley Database https://www2.eecs.berkeley.edu/Research/Projects/CS/vision/bsds/. Accessed 15 May 2020
Chen SD, Ramli AR (2003) Contrast enhancement using recursive mean-separate histogram equalization for scalable brightness preservation. IEEE Trans Consum Electron 49(4):1301–1309. https://doi.org/10.1109/TCE.2003.1261233
Chen SD, Ramli AR (2003) Minimum mean brightness error bi-histogram equalization in contrast enhancement. IEEE Trans Consum Electron 49(4):1310–1319. https://doi.org/10.1109/TCE.2003.1261234
Chen Z, Abidi BR, Page DL et al (2006) Gray-level grouping (GLG): an automatic method for optimized image contrast enhancement – part I: the basic method. IEEE Trans Image Process 15(8):2290–2302. https://doi.org/10.1109/TIP.2006.875204
CVG-UGR database http://decsai.ugr.es/cvg/dbimagenes/index.php/. Accessed 15 May 2020
Fan DP, Cheng MM, Liu JJ et al (2018) Salient objects in clutter: bringing salient object detection to the foreground. Computer Vision – ECCV 2018:196–212. https://doi.org/10.1007/978-3-030-01267-0_12
Gong C, et al (2015) Saliency propagation from simple to difficult. 2015 IEEE conference on computer vision and pattern recognition https://doi.org/10.1109/CVPR.2015.7298868
Gonzalez RC, Woods RE (2007) Digital image processing. Pearson Education, New York https://doi.org/10.1109/IEMDC.2013.6556306
Hoseini P, Shayesteh MG (2013) Efficient contrast enhancement of images using hybrid ant colony optimisation, genetic algorithm, and simulated annealing. Digital Signal Process 23(3):879–893. https://doi.org/10.1016/j.dsp.2012.12.011
Huang SC, Cheng FC, Chiu YS (2013) Efficient contrast enhancement using adaptive gamma correction with weighting distribution. IEEE Trans Image Process 22(3):1032–1041. https://doi.org/10.1109/TIP.2012.2226047
Keren F et al (2019) Deepside: a general deep framework for salient object detection. Neurocomputing 356:69–82. https://doi.org/10.1016/neucom.2019.04.062
Khan MF, Khan E, Abbasi ZA (2014) Segment dependent dynamic multi-histogram equalization for image contrast enhancement. Digital Signal Process 25:198–223. https://doi.org/10.1016/j.dsp.2013.10.015
Kim YT (1997) Contrast enhancement using brightness preserving bi-histogram equalization. IEEE Trans Consum Electron 43(1):1–8. https://doi.org/10.1109/30.580378
Kodak database http://r0k.us/graphics/kodak/. Accessed 15 May 2020
Kumar SP, Kumar MS, Rajeesh J (2013) Palmprint enhancement using recursive histogram equalisation. Imaging Sci J 61(5):447–457. https://doi.org/10.1179/1743131X12Y.0000000031
Kumarbarode M, Kumar Rai R, Murarka S (2015) DWT curvet based dynamic histogram equalization for brightness preserving contrast enhancement of images. Int J Comput Vis 110(13):32–36. https://doi.org/10.5120/19380-1086
Mahmood A, Khan SA, Hussain S, Almaghayreh EM (2019) An adaptive image contrast enhancement technique for low-contrast images. IEEE Access 7:161584–161593. https://doi.org/10.1109/ACCESS.2019.2951468
Menotti D, Najman L, Facon J, Araujo AA (2007) Multi-histogram equalization methods for contrast enhancement and brightness preserving. IEEE Trans Consum Electron 53(3):1186–1194. https://doi.org/10.1109/TCE.2007.4341603
Muslim HSM, Khan SA, Hussain S, Jamal A, Qasim HSA (2018) A knowledge-based image enhancement and denoising approach. Comput Math Organ Theory 25(2):108–121. https://doi.org/10.1007/s10588-018-9274-8
Ooi CH, Isa NAM (2007) Adaptive contrast enhancement methods with brightness preserving. IEEE Trans Consum Electron 56(4):2543–2551. https://doi.org/10.1109/TCE.2010.5681139
Ooi CH, Kong NSP, Ibrahim H (2009) Bi-histogram equalization with plateau limit for digital image enhancement. IEEE Trans Consum Electron 55(4):2072–2080. https://doi.org/10.1109/TCE.2009.5373771
Parihar AS, Verma OP (2017) Contrast enhancement using entropy-based dynamic sub-histogram equalization. IET Image Process 10(11):799–808. https://doi.org/10.1049/iet-ipr.2016.0242
Rao Y, Chen L (2012) A survey of video enhancement techniques. J Information Hiding and Multimedia Signal Process 3(1):71–99
Sengee N, Sengee A, Choi HK (2010) Image contrast enhancement using bi-histogram equalization with neighborhood metrics. IEEE Trans Consum Electron 56(4):2727–2734. https://doi.org/10.1109/TCE.2010.5681162
Sim KS, Tso CP, Tan YY (2007) Recursive sub-image histogram equalization applied to gray scale images. Pattern Recogn Lett 28(10):1209–1221. https://doi.org/10.1016/j.patrec.2007.02.003
USC-SIPI database http://sipi.usc.edu/database/. Accessed 15 May 2020
Wang Y, Chen Q, Zhang B (1999) Image enhancement based on equal area dualistic sub-image histogram equalization method. IEEE Trans Consum Electron 45(1):68–75. https://doi.org/10.1109/30.754419
Wang Z, Bovik AC, Sheikh HR et al (2004) Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process 13(4):600–612. https://doi.org/10.1109/TIP.2003.819861
Yao Z, Zhou Q, Yang X et al (2016) Quadrants histogram equalization with a clipping limit for image enhancement. 2016 8th international conference on Wireless Communications & Signal Processing. (WCSP) IEEE. https://doi.org/10.1109/WCSP.2016.7752466
Zhao JX, Liu JJ, Fan DP, et al (2019) EGNet: edge guidance network for salient object detection. IEEE 2019 conference on computer vision and pattern recognition https://doi.org/10.1109/CVPR.2019.00887
Acknowledgments
This paper was supported by the National Natural Science Foundation of China (No. 61674115) and the Natural Science Foundation of Tianjin, China (No.17JCYBJC15900).
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare no conflict of interest.
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Cao, Q., Shi, Z., Wang, R. et al. A brightness-preserving two-dimensional histogram equalization method based on two-level segmentation. Multimed Tools Appl 79, 27091–27114 (2020). https://doi.org/10.1007/s11042-020-09265-y
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11042-020-09265-y