Advertisement

An optimal adaptive thresholding based sub-histogram equalization for brightness preserving image contrast enhancement

  • Pankaj Kandhway
  • Ashish Kumar BhandariEmail author
Article
First Online:
  • 2 Downloads

Abstract

In this paper, a new adaptive thresholding based sub-histogram equalization (ATSHE) scheme is proposed for contrast enhancement and brightness preservation with retention of basic image features. The histogram of an input image is divided into different sub-histogram using adaptive thresholding intensity values. The number of threshold values or sub-histograms of the image are not fixed, but depends on the peak signal-to-noise ratio (PSNR) of the thresholded image. Histogram clipping is also used here to control the undesired enhancement of resultant image thus avoiding over-enhancement. Median value of the original histogram gives the threshold value of clipping process. The main objective of proposed method is to improve contrast enhancement with preservation of mean brightness value, structural similarity index (SSIM) and information content of the images. Image contrast enhancement is examined by well-known enhancement assessment parameters such as contrast per pixel and modified measure of enhancement. The mean brightness preservation of the image is evaluated by using absolute mean brightness error value and feature preservation qualities are checked through SSIM and PSNR values. Through the proposed routine, the enhanced images achieve a good trade-off between features enhancement, low contrast boosting and brightness preservation in addition with the natural feel of the original image. In particular, the proposed ATSHE scheme due to its adaptive nature of threshold selection can successfully enhance images under oodles of weak illumination situations such as backlighting effects, non-uniform illumination low contrast and dark images.

Keywords

Adaptive thresholding Brightness preservation Contrast enhancement Peak signal-to-noise ratio Sub-histogram equalization Color satellite images 
This is a preview of subscription content, log in to check access.

Notes

Acknowledgements

The authors wish to thank the editors and anonymous referees for their constructive criticism and valuable suggestions.

References

  1. Abdullah-Al-Wadud, M. (2007). A dynamic histogram equalization for image contrast enhancement. IEEE Transactions on Consumer Electronics, 53, 593–600.CrossRefGoogle Scholar
  2. Agaian, S. S., Silver, B., & Panetta, K. A. (2007). Transform coefficient histogram-based image enhancement algorithms using contrast entropy. IEEE Transactions on Image Processing, 16(3), 741–758.MathSciNetCrossRefGoogle Scholar
  3. Arora, S., Acharya, J., Verma, A., & Panigrahi, P. K. (2008). Multilevel thresholding for image segmentation through a fast statistical recursive algorithm. Pattern Recognition Letters, 29(2), 119–125.CrossRefGoogle Scholar
  4. Bhandari, A. K., Kumar, A., Chaudhary, S., & Singh, G. K. (2017). A new beta differential evolution algorithm for edge preserved colored satellite image enhancement. Multidimensional Systems and Signal Processing, 28(2), 495–527.CrossRefzbMATHGoogle Scholar
  5. Bhandari, A. K., Kumar, A., & Padhy, P. K. (2011). Enhancement of low contrast satellite images using discrete cosine transform and singular value decomposition. World Academy of Science, Engineering and Technology, 55, 35–41.Google Scholar
  6. Bhandari, A. K., Kumar, A., Singh, G. K., & Soni, V. (2016). Dark Satellite image enhancement using knee transfer function and gamma correction based on DWT-SVD. Multidimensional System and Signal Process., 27(2), 453–476.CrossRefGoogle Scholar
  7. Bhandari, A. K., Maurya, S., & Meena, A. K. (2018). Social spider optimization based optimally weighted Otsu thresholding for image enhancement. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.  https://doi.org/10.1109/JSTARS.2018.2870157.Google Scholar
  8. Bhandari, A. K., Singh, V. K., Kumar, A., & Singh, G. K. (2014a). Cuckoo search algorithm and wind driven optimization based study of satellite image segmentation for multilevel thresholding using Kapur’s entropy. Expert Systems with Applications, 41(7), 3538–3560.CrossRefGoogle Scholar
  9. Bhandari, A. K., Soni, V., Kumar, A., & Singh, G. K. (2014b). Cuckoo search algorithm based satellite image contrast image and brightness enhancement using DWT-SVD. ISA Transactions, 53(4), 1286–1296.CrossRefGoogle Scholar
  10. Celik, T., & Tjahjadi, T. (2010). Unsupervised colour image segmentation using dual-tree complex wavelet transform. Computer Vision and Image Understanding, 114(7), 813–826.CrossRefGoogle Scholar
  11. Chen, S.-D., & Ramli, A. R. (2003a). Minimum mean brightness error bi-histogram equalization in contrast enhancement. IEEE Transactions on Consumer Electronics, 49(4), 1310–1319.CrossRefGoogle Scholar
  12. Chen, S.-D., & Ramli, A. R. (2003b). Contrast enhancement using recursive-mean-separate histogram equalization for scalable brightness preservation. IEEE Transactions on Consumer Electronics, 49(4), 1301–1309.CrossRefGoogle Scholar
  13. Cheng, H.-D., & Xu, H. (2000). A novel fuzzy logic approach to contrast enhancement. Pattern Recognition, 33(5), 809–819.CrossRefGoogle Scholar
  14. Demirel, H., Ozcinar, C., & Anbarjafari, G. (2010). Satellite image contrast enhancement using discrete wavelet transform and singular value decomposition. IEEE Geoscience and Remote Sensing Letters, 7(2), 333–337.CrossRefGoogle Scholar
  15. Fu, X., Liao, Y., Zeng, D., Huang, Y., Zhang, X.-P., & Ding, X. (2015). A probabilistic method for image enhancement with simultaneous illumination and reflectance estimation. IEEE Transactions on Image Processing, 24(12), 4965–4977.MathSciNetCrossRefGoogle Scholar
  16. Fu, X., Zeng, D., Huang, Y., Liao, Y., Ding, X., & Paisley, J. (2016). A fusion-based enhancing method for weakly illuminated images. Signal Processing, 129, 82–96.CrossRefGoogle Scholar
  17. Gonzalez, R. C., & Woods, R. E. (2011). Digital image processing (3rd ed.). Upper Saddle River: Pearson Prentice Hall.Google Scholar
  18. Hasikin, K., & Isa, N. A. M. (2014). Adaptive fuzzy contrast factor enhancement technique for low contrast and nonuniform illumination images. Signal, Image and Video Processing, 8(8), 1591–1603.CrossRefGoogle Scholar
  19. 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.CrossRefGoogle Scholar
  20. Huang, S.-C., & Yeh, C.-H. (2013). Image contrast enhancement for preserving mean brightness without losing image features. Engineering Applications of Artificial Intelligence, 26(5), 1487–1492.CrossRefGoogle Scholar
  21. Kim, Y. T. (1997). Contrast enhancement using brightness preserving bi-histogram equalization. IEEE Transactions on Consumer Electronics, 43, 1–8.CrossRefGoogle Scholar
  22. Kong, N. S. P., Ibrahim, H., Ooi, C. H., Chieh, D. C. J. (2009). Enhancement of microscopic images using modified self-adaptive plateau histogram equalization. In International conference on comput. computer technology and development, 2009 (Vol. 308–310).Google Scholar
  23. Kong, T. L., & Isa, N. A. M. (2017). Bi-histogram modification method for non-uniform illumination and low-contrast images. Multimedia Tools and Applications, 77, 8955–8978.CrossRefGoogle Scholar
  24. Lai, Y.-R., Tsai, P.-C., Yao, C.-Y., & Ruan, S.-J. (2017). Improved local histogram equalization with gradient-based weighting process for edge preservation. Multimedia Tools and Applications, 76, 1585–1613.CrossRefGoogle Scholar
  25. Li, C., & Bovik, A. C. (2010). Content-partitioned structural similarity index for image quality assessment. Signal Processing: Image Communication, 25(7), 517–526.Google Scholar
  26. Liu, B., Jin, W., Chen, Y., Liu, C., & Li, L. (2011). Contrast enhancement using non-overlapped sub-blocks and local histogram projection. IEEE Transactions on Consumer Electronics, 57(2), 583–588.CrossRefGoogle Scholar
  27. Niu, Y., Wu, X., & Shi, G. (2016). Image enhancement by entropy maximization and quantization resolution upconversion. IEEE Transactions on Image Processing, 25, 4815–4828.MathSciNetCrossRefGoogle Scholar
  28. Ooi, C. H., & Isa, N. A. M. (2010). Quadrants dynamic histogram equalization for contrast enhancement. IEEE Transactions on Consumer Electronics, 56, 2552–2559.CrossRefGoogle Scholar
  29. Peli, E. (1990). Contrast in complex images. JOSA A, 7(10), 2032–2040.CrossRefGoogle Scholar
  30. Priyadharsini, R., Sharmila, T. S., & Rajendran, V. (2018). A wavelet transform based contrast enhancement method for underwater acoustic images. Multidimensional Systems and Signal Processing, 29(4), 1845–1859.CrossRefGoogle Scholar
  31. Sangee, N., Sangee, A., & Choi, H. K. (2010). Image contrast enhancement using bi-histogram equalization with neighbourhood metrics. IEEE Transactions on Consumer Electronics, 56(4), 2552–2559.CrossRefGoogle Scholar
  32. Santhi, K., & Wahida Banu, R. S. D. (2015). Adaptive contrast enhancement using modified histogram Equalization. Optik, 126, 1809–1814.CrossRefGoogle Scholar
  33. Shakeri, M., Dezfoulian, M. H., Khotanlou, H., Barati, A. H., & Masoumi, Y. (2017). Image contrast enhancement using fuzzy clustering with adaptive cluster parameter and sub-histogram equalization. Digital Signal Processing, 62, 224–237.CrossRefGoogle Scholar
  34. Shanmugavadivu, P., & Balasubramanian, K. (2014). Thresholded and optimized histogram equalization for contrast enhancement of images. Computers & Electrical Engineering, 40, 757–768.CrossRefGoogle Scholar
  35. Sheet, D., Garud, H., Suveer, A., Mahadevappa, M., & Chatterjee, J. (2010). Brightness preserving dynamic fuzzy histogram equalization. IEEE Transactions on Consumer Electronics, 56, 2475–2480.CrossRefGoogle Scholar
  36. Sidike, P., Sagan, V., Qumsiyeh, M., Maimaitijiang, M., Essa, A., & Asari, V. (2018). Adaptive trigonometric transformation function with image contrast and color enhancement: Application to unmanned aerial system imagery. IEEE Geoscience and Remote Sensing Letters, 15(3), 404–408.CrossRefGoogle Scholar
  37. Sim, K. S., Tso, C. P., & Tan, Y. Y. (2007). Recursive sub-image histogram equalization applied to gray scale images. Pattern Recognition Letters, 28(10), 1209–1221.CrossRefGoogle Scholar
  38. Singh, K., & Kapoor, R. (2014a). Image enhancement using exposure based sub image histogram equalization. Pattern Recognition Letters, 36, 10–14.CrossRefGoogle Scholar
  39. Singh, K., & Kapoor, R. (2014b). Image enhancement via median-mean based sub-image-clipped histogram equalization. Optik, 125, 4646–4651.CrossRefGoogle Scholar
  40. Singh, K., Kapoor, R., & Sinha, S. K. (2015). Enhancement of low exposure images via recursive histogram equalization algorithms. Optik, 126, 2619–2625.CrossRefGoogle Scholar
  41. Singh, K., Vishwakarma, D. K., Walia, G. S., & Kapoor, R. (2016). Contrast enhancement via texture region based histogram equalization. Journal of Modern Optics, 63(15), 1444–1450.CrossRefGoogle Scholar
  42. Sundaram, M., Ramar, K., Arumugam, N., & Prabin, G. (2011). Histogram modified local contrast enhancement for mammogram images. Applied Soft Computing, 11(8), 5809–5816.CrossRefGoogle Scholar
  43. Tang, J. R., & Isa, N. A. M. (2017). Bi-histogram equalization using modified histogram bins. Applied Soft Computing, 55, 31–43.CrossRefGoogle Scholar
  44. Thum, C. (1984). Measurement of the entropy of an image with application to image focusing. Optica Acta: International Journal of Optics, 31(2), 203–211.MathSciNetCrossRefGoogle Scholar
  45. Wan, Y., Chen, Q., & Zhang, B. M. (1999). Image enhancement based on equal area dualistic sub-image histogram equalization method. IEEE Transactions on Consumer Electronics, 45, 68–75.CrossRefGoogle Scholar
  46. Wang, X., & Chen, L. (2017). An effective histogram modification scheme for image contrast enhancement. Signal Processing: Image Communication, 58, 187–198.Google Scholar
  47. Wong, C. Y., Jiang, G., Rahman, M. A., Liu, S., Lin, S. C.-F., Kwok, N., et al. (2016). Histogram equalization and optimal profile compression based approach for colour image enhancement. Journal of Visual Communication and Image Representation, 38, 802–813.CrossRefGoogle Scholar
  48. Xiao, Y., Cao, Z., & Yuan, J. (2014). Entropic image thresholding based on GLGM histogram. Pattern Recognition Letters, 40(15), 47–55.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Electronics and Communication EngineeringNational Institute of Technology PatnaPatnaIndia

Personalised recommendations

Cite article

Buy options