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3D color channel based adaptive contrast enhancement using compensated histogram system

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Abstract

Low contrast image is one of the major challenges in photography. The low contrast image not only poses difficulty to the interpretation of the scene but also causes trouble in the onward processing of the image for computer vision tasks. Histogram equalization (HE) is a traditional and widely used approach for contrast enhancement and applies to almost all types of images. However, HE causes over-enhancement of the image which degrades its natural appearance. In this paper, a novel scheme for enhancing the image contrast while retaining its naturalness has been proposed. The proposed method uses the compensated histogram equalization technique on each channel individually followed by blending of the channels with a suitable adaptive brightness adjustment kernel. The three color channels are combined to form the intermediate image. The high-frequency noise introduced during the process is filtered out. Finally, adaptive power law transformation is applied to adjust the overall brightness and to retain its naturalness. This makes the method strong in terms of contrast enhancement along with details preservation. The proposed method is applicable to all the contrast degraded images as it automatically adjusts its parameters based on the degradation level. The simulation results, on the CSIQ dataset, show that the proposed method performs better, qualitatively, and quantitatively than the existing methods.

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References

  1. Bhandari, A.K., Kandhway, P., Maurya, S.: Salp swarm algorithm based optimally weighted histogram framework for image enhancement. IEEE Trans. Instrum. Meas. 69(9), 6807–6815 (2020)

    Article  Google Scholar 

  2. Qu, Z., Huang, X., Liu, L.: An improved algorithm of multi-exposure image fusion by detail enhancement. Multimed. Syst. 1–12 (2020)

  3. Liu, J., Ge, J., Xue, Y., He, W., Sun, Q., Li, S.: Multi-scale skip-connection network for image super-resolution. Multimed. Syst. 1–16 (2020)

  4. Meng, H., Yan, Y., Cai, C., Qiao, R., Wang, F.: A hybrid algorithm for underwater image restoration based on color correction and image sharpening. Multimed. Syst. 1–11 (2020)

  5. Srinivas, K., Bhandari, A.K., Singh, A.: Low-contrast image enhancement using spatial contextual similarity histogram computation and color reconstruction. J. Franklin Inst. 357(18), 13941–13963 (2020)

    Article  Google Scholar 

  6. Gonzalez, R.C., Woods, R.E.: Digital Image Processing, 2nd edn. Addison-Wesley, Reading (1992)

    Google Scholar 

  7. Pizer, S.M., Amburn, E.P., Austin, J.D., Cromartie, R., Geselowitz, A., Greer, T., ter Haar Romeny, B., Zimmerman, J.B., Zuiderveld, K.: Adaptive histogram equalization and its variations. Comput. Vis. Graph. Image Process. 39(3), 355–368 (1987)

    Article  Google Scholar 

  8. Pizer, S.M., Amburn, E.P., Austin, J.D., Cromartie, R., Geselowitz, A., Greer, T., Romeny, B.H., Zimmerman, J.B., Zuiderveld, K.: Adaptive histogram equalization and its variations. Comput. Vis. Graph. Image Process. 39, 355–368 (1987)

    Article  Google Scholar 

  9. Pizer, S.M., Johnston, R.E., Ericksen, J.P., Yankaskas, B.C., Muller, K.E.: Contrast limited adaptive histogram equalization: speed and effectiveness. In: IEEE Conference on Visualization in Biomedical Computing (VBC ’90), pp. 337–345 (1990)

  10. Kim, Y.-T.: Contrast enhancement using brightness preserving bi-histogram equalization. IEEE Trans. Consum. Electron. 43(1), 1–8 (1997)

    Article  Google Scholar 

  11. Wang, Y., Chen, Q., Zhang, B.: Image enhancement based on equal area dualistic sub-image histogram equalization method. IEEE Trans. Consum. Electron. 45(1), 68–75 (1999)

    Article  Google Scholar 

  12. Chen, S.D., Ramli, A.: Contrast enhancement using recursive mean separate histogram equalization for scalable brightness preservation. IEEE Trans. Consum. Electron. 49, 1301–1309 (2003)

    Article  Google Scholar 

  13. Chen, S.D., Ramli, A.: Minimum mean brightness error bi-histogram equalization in contrast enhancement. IEEE Trans. Consum. Electron. 49, 1310–1319 (2003)

    Article  Google Scholar 

  14. Sim, K.S., Tso, C.P., Tan, Y.Y.: Recursive sub-image histogram equalization applied to grayscale images. ScienceDirect Pattern Recogn. Lett. 28(10), 1209–1221 (2007)

    Article  Google Scholar 

  15. Kim, M., Chung, M.G.: Recursively separated and weighted histogram equalization for brightness preservation and contrast enhancement. IEEE Trans. Consum. Electron. 54, 1389–1397 (2008)

    Article  Google Scholar 

  16. Wadud, M.A., Kabir, H., Dewan, M.A., Chae, O.: A dynamic histogram equalization for image contrast enhancement. IEEE Trans. Consum. Electron. 53, 593–599 (2007)

    Article  Google Scholar 

  17. Wang, Q., Ward, R.K.: Fast image/video contrast enhancement based on weighted threshold histogram equalization. IEEE Trans. Consum. Electron. 53, 757–764 (2007)

    Article  Google Scholar 

  18. Ibrahim, H., Kong, N.S.: Brightness preserving dynamic histogram equalization for image contrast enhancement. IEEE Trans. Consum. Electron. 53, 1752–1758 (2007)

    Article  Google Scholar 

  19. Ooi, C.H., Pik Kong, N.S., Ibrahim, H.: Bi-histogram equalization with a plateau limit for digital image enhancement. IEEE Trans. Consum. Electron. 55(4), 2072–2080 (2009)

    Article  Google Scholar 

  20. Ooi, C.H., Isa, N.A.: Quadrants dynamic histogram equalization for contrast enhancement. IEEE Trans. Consum. Electron. 56, 2552–2559 (2010)

    Article  Google Scholar 

  21. Liu, S., Zhang, Y.: Detail-preserving underexposed image enhancement via optimal weighted multi-exposure fusion. IEEE Trans. Consum. Electron. 65(3), 303–311 (2019)

    Article  Google Scholar 

  22. Srinivas, K., Bhandari, A.K., Singh, A.: Exposure-based energy curve equalization for enhancement of contrast distorted images. IEEE Trans. Circuits Syst. Video Technol. 30, 4663–4675 (2019)

    Article  Google Scholar 

  23. Kumar, M., Bhandari, A.K.: Contrast enhancement using novel white balancing parameter optimization for perceptually invisible images. IEEE Trans. Image Process. 29, 7525–7536 (2020)

    Article  Google Scholar 

  24. Lore, K.G., Akintayo, A., Sarkar, S.: LLNet: a deep autoencoder approach to natural low-light image enhancement. Pattern Recogn. 61, 650–662 (2017)

    Article  Google Scholar 

  25. Srinivas, K., Bhandari, A.K.: Low light image enhancement with adaptive sigmoid transfer function. IET Image Proc. 14(4), 668–678 (2019)

    Article  Google Scholar 

  26. Ren, W., et al.: Low-light image enhancement via a deep hybrid network. IEEE Trans. Image Process. 28(9), 4364–4375 (2019)

    Article  MathSciNet  Google Scholar 

  27. Gharbi, M., Chen, J., Barron, J.T., Hasinoff, S.W., Durand, F.: Deep bilateral learning for real-time image enhancement. ACM Trans.. Graph. (TOG) 36(4), 1–12 (2017)

    Article  Google Scholar 

  28. Bhandari, A. K., Shahnawazuddin, S., & Meena, A. K.: A novel fuzzy clustering based histogram model for image contrast enhancement. IEEE Trans. Fuzzy Syst. (2019)

  29. Raju, G., Nair, M.S.: A fast and efficient color image enhancement method based on fuzzy-logic and histogram. AEU Int. J. Electron. Commun. 68(3), 237–243 (2014)

    Article  Google Scholar 

  30. Yan, C., Gong, B., Wei, Y., Gao, Y.: Deep multi-view enhancement hashing for image retrieval. In: IEEE Transactions on Pattern Analysis and Machine Intelligence (2020)

  31. Yan, C., Shao, B., Zhao, H., Ning, R., Zhang, Y., Xu, F.: 3D room layout estimation from a single RGB image. In: IEEE Transactions on Multimedia (2020)

  32. Yan, C., Li, Z., Zhang, Y., Liu, Y., Ji, X., Zhang, Y.: Depth image denoising using nuclear norm and learning graph model. In: ACM Trans. on Multimedia Computing Communications and Applications (2020)

  33. Ooi, C.H., Mat Isa, N.A.: Adaptive contrast enhancement methods with brightness preserving. IEEE Trans. Consum. Electron. 56(4), 2543–2551 (2010)

    Article  Google Scholar 

  34. Chang, Y., Chang, C.: A simple histogram modification scheme for contrast enhancement. IEEE Trans. Consum. Electron. 56(2), 737–742 (2010)

    Article  Google Scholar 

  35. Huang, S.C., Yeh, C.H.: Image contrast enhancement for preserving mean brightness without losing image features. Eng. Appl. Artif. Intell. 26(5–6), 1487–1492 (2013)

    Article  Google Scholar 

  36. Thum, Ch.: Measurement of the entropy of an image with application to image focusing. Opt. Acta Int. J. Opt. 31(2), 203–211 (1984). https://doi.org/10.1080/713821475

    Article  MathSciNet  Google Scholar 

  37. Video Quality Experts Group: Final report from the video quality experts group on the validation of objective models of video quality assessment. In: VQEG meeting, Ottawa, Canada, March (2000)

  38. Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: From error visibility to structural similarity. IEEE Trans. Image Process. 13(4) (2004)

  39. Eramian, M., Mould, D.: Histogram equalization using neighborhood metrics. In: IEEE, The 2nd Canadian Conference on Computer and Robot Vision (CRV'05), Victoria, BC, Canada, 2005, pp. 397–404

  40. Xue, W., Zhang, L., Mou, X., Bovik, A.C.: Gradient magnitude similarity deviation: a highly efficient perceptual image quality index. IEEE Trans. Image Process. 23(2), 684–695 (2014)

    Article  MathSciNet  Google Scholar 

  41. Agaian, S.S., Silver, B., Panetta, K.A.: Transform coefficient histogram-based image enhancement algorithms using contrast entropy. IEEE Trans. Image Process. 16(3), 741–758 (2007)

    Article  MathSciNet  Google Scholar 

  42. Wang, X., Chen, L.: An effective histogram modification scheme for image contrast enhancement. Signal Process. Image Commun. 58, 187–198 (2017)

    Article  Google Scholar 

  43. Mittal, A., Soundararajan, R., Bovik, A.C.: Making a “Completely Blind” image quality analyzer. IEEE Signal Process. Lett. 20(3), 209–212 (2013)

    Article  Google Scholar 

  44. Venkatanath, N., Praneeth, D., Maruthi Chandrasekhar B., Channappayya, S.S., Medasani, S. S.: Blind image quality evaluation using perception based features. In: 2015 Twenty First National Conference on Communications (NCC), Mumbai, 2015, pp. 1–6

  45. Sheikh, H. R., Wang, Z., Cormack, L., Bovik, A.C.: LIVE Image Quality Assessment Database Release 2. https://live.ece.utexas.edu/research/quality. Accessed 15 Aug 2020

  46. Mittal, A., Moorthy, A. K., Bovik, A. C.: Blind/Referenceless Image Spatial Quality Evaluator. In: 2011 Conference Record of the Forty Fifth Asilomar Conference on Signals, Systems and Computers (ASILOMAR), Pacific Grove, CA, 2011, pp. 723–727

  47. Available [Online]: http://vision.eng.shizuoka.ac.jp/mod/page/view.php?id=23. Accessed 15 Aug 2020

  48. Larson, E.C., Chandler, D.M.: Most apparent distortion: Full-reference image quality assessment and the role of strategy. J. Electron. Imaging 19(1), 011006 (2010)

    Article  Google Scholar 

  49. Otsu, N.: A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man Cybern. 9(1), 62–66 (1979)

    Article  MathSciNet  Google Scholar 

  50. Wang, C., Ye, Z.: Brightness preserving histogram equalization with maximum entropy: a variational perspective. IEEE Trans. Consum. Electron. 51(4), 1326–1334 (2005)

    Article  Google Scholar 

  51. Lee, C., Lee, C., Kim, C.-S.: Contrast enhancement based on layered difference representation of 2D histograms. IEEE Trans. Image Process. 22(12), 5372–5384 (2013)

    Article  Google Scholar 

  52. The Berkeley Segmentation Dataset and Benchmark, 2018. [Online]. Available: https://www2.eecs.berkeley.edu/Research/Projects/CS/vision/bsds/. Accessed 15 Aug 2020

  53. Franzen, R.: Kodak lossless true color image suite. 2018. [Online]. http://r0k.us/graphics/kodak/. Accessed 13 Dec 2017

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Correspondence to Ashish Kumar Bhandari.

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Kumar, A., Bhandari, A.K. & Kumar, R. 3D color channel based adaptive contrast enhancement using compensated histogram system. Multimedia Systems 27, 563–580 (2021). https://doi.org/10.1007/s00530-021-00757-x

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