Abstract
Image enhancement is a crucial pre-processing technique for image processing applications such as satellite images, medical images, and aerial surveillance systems. The image enhancement aims to produce visual content of the image in more pleasing and more suitable for machine vision applications. Enhancement of image can be improved by either increasing the contrast of the image with a low dynamic range or by highlighting the prominent details of the image. In this paper, a comprehensive analysis of bi-histogram and two-dimensional histogram equalization-based contrast enhancement techniques is performed. The performance of the various algorithms has been validated through three different databases and various performance measures. From the compendious analysis, it can be interpreted that modeling an algorithm with lower absolute mean brightness error (AMBE) and higher contrast values can render a better-enhanced image. From the qualitative and quantitative analysis, edge enhancing bi-histogram equalization using guided image filter outperforms the other contrast enhancement techniques.
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References
Gonzalez CR (2012) Digital inage processing. Pearson Prentice Hall Publisher
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
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
Sim K, Tso C, Tan Y (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
Ooi CH, Pik Kong NS, Ibrahim H (2009) Bi-histogram equalization with a plateau limit for digital image enhancement. IEEE Trans Consum Electron 55(4):2072–2080. https://doi.org/10.1109/TCE.2009.5373771
Khan MF, Khan E, Abbasi Z (2014) Segment selective dynamic histogram equalization for brightness preserving contrast enhancement of images. Optik 125(3):1385–1389. https://doi.org/10.1016/j.ijleo.2013.08.005
Chen S-D, 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
Celik T (2014) Spatial entropy-based global and local image contrast enhancement. IEEE Trans Image Proc 23(12):5298–5308. https://doi.org/10.1109/TIP.2014.2364537
Singh K, Kapoor R (2014) Image enhancement using exposure based sub image histogram equalization. Pattern Recogn Lett 36:10–14. https://doi.org/10.1016/j.patrec.2013.08.024
Tang JR, Isa NAM (2014) Adaptive image enhancement based on bi-histogram equalization with a clipping limit. Comput Electr Eng 40(8):86–103. https://doi.org/10.1016/j.compeleceng.2014.05.017
Celik T, Li HC (2016) Residual spatial entropy-based image contrast enhancement and gradient-based relative contrast measurement. J Modern Opt 63(16):1600–1617. https://doi.org/10.1080/09500340.2016.1163427
Wang X, Chen L (2018) Contrast enhancement using feature-preserving bi-histogram equalization. Sign Image and Video Proc 12(4):685–692. https://doi.org/10.1007/s11760-017-1208-2
Mun J, Jang Y, Nam Y, Kim J (2019) Edge-enhancing bi-histogram equalisation using guided image filter. J Visual Commun Image Represent 58:688–700. https://doi.org/10.1016/j.jvcir.2018.12.037
Ng TT, Chang SF, Hsu J, Pepeljugoski M (2004) Columbia photographic images and photorealistic computer graphics dataset. Tech Rep 205-2004-5, ADVENT, Columbia University
USC-SIPI Database: http://sipi.usc.edu/database/
Larson EC, Chandler MC (2010) Most apparent distortion: full-reference image quality assessment and the role of strategy. J Electron Imag 19(1):1–21. https://doi.org/10.1117/1.3267105
Ooi CH, Mat Isa NA (2010) Adaptive contrast enhancement methods with brightness preserving. IEEE Trans Consum Electron 56(4):2543–2551
Kar M, Giritharan R, Elangovan P, Kumar M (2019) Analysis of diagnostic features from fundus image using multiscale wavelet decomposition. ICIC Express Lett, Part B: Appl 10:175–184 https://doi.org/10.24507/icicelb.10.02.175
Liang K, Ma Y, Xie Y, Zhou B, Wang R (2012) A new adaptive contrast enhancement algorithm for infrared images based on double plateaus histogram equalization. Infrared Phys Technol 55(4):309–315
Li C, Bovik AC (2010) Content-partitioned structural similarity index for image quality assessment. Sign Proc: Image Commun 25(7):517–526
Shannon CE (1948) A mathematical theory of communication. Bell Syst Techn J 27(3):379–423
Nath MK, Dandapat S (2012) Differential entropy in wavelet sub-band for assessment of glaucoma. Int J Imag Syst Technol 22:161–165
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Vijayalakshmi, D., Nath, M.K. (2021). A Compendious Analysis of Advances in HE Methods for Contrast Enhancement. In: Harvey, D., Kar, H., Verma, S., Bhadauria, V. (eds) Advances in VLSI, Communication, and Signal Processing. Lecture Notes in Electrical Engineering, vol 683. Springer, Singapore. https://doi.org/10.1007/978-981-15-6840-4_26
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DOI: https://doi.org/10.1007/978-981-15-6840-4_26
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