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Adaptive gamma correction for contrast enhancement of remote sensing images

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Abstract

Contrast magnification is a critical parameter of image enhancement. Remote sensing images are distance captured images, so they naturally have very low contrast as compared to other images. In this paper a very simple and efficient technique based on adaptive gamma correction and Discrete cosine transform(DCT) is proposed which can bring out the maximum information present in the image and enhance the contrast of the image very well. We first apply an adaptive gamma correction which enhances the image globally and than high frequency components of DCT transformation are further altered to intensify the minute details of the image. Proposed method is judged on the basis of visual quality and numerical parameters compared with the other existing techniques. The results obtained for proposed technique surpasses all the other methods both qualitatively and quantitatively.

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References

  1. Agaian SS, Lentz KP, Grigoryam AM (2000) A new measure of image enhancement. In: IASTED international conference on signal processing and communication

  2. Atta R, Ghanbari M (2013) Low-contrast satellite images enhancement using discrete cosine transform pyramid and singular value decomposition. IET Image Process 7(5):472–483

    Article  Google Scholar 

  3. Cao G, Zhao Y, Ni R, Li X (2014) Contrast enhancement-based forensics in digital images. IEEE Trans Inf Forens Secur 9(3):515–525

    Article  Google Scholar 

  4. Celik T (2014) Spatial entropy-based global and local image contrast enhancement. IEEE Trans Image Process 23(12):5298–5308

    Article  MathSciNet  MATH  Google Scholar 

  5. Celik T, Tjahjadi T (2011) Contextual and variational contrast enhancement. IEEE Trans Image Process 20(12):3431–3441

    Article  MathSciNet  MATH  Google Scholar 

  6. Demirel H, Ozcinar C, Anbarjafari G (2010) Satellite image contrast enhancement using discrete wavelet transform and singular value decomposition. IEEE Geosci Remote Sens Lett 7(2):333–337

    Article  Google Scholar 

  7. Fu X, Wang J, Zeng D, Huang Y, Ding X (2015) Remote sensing image enhancement using regularized-histogram equalization and DCT. IEEE Geosci Remote Sens Lett 12(11):2301–2305

    Article  Google Scholar 

  8. Fu X, Zeng D, Huang Y, Liao Y, Ding X, Paisley J (2016) A fusion-based enhancing method for weakly illuminated images. Signal Process Elsevier 129:82–96

    Article  Google Scholar 

  9. 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

    Article  MathSciNet  MATH  Google Scholar 

  10. Immerker J (1996) Fast noise variance estimation. Comput Vis Image Underst 64(2):300–3012

    Article  Google Scholar 

  11. Jang JH, Kim SD, Ra JB (2011) Enhancement of optical remote sensing images by sub band-decomposed multiscale retinex with hybrid intensity transfer function. IEEE Geosci Remote Sens Lett 8(5):983– 987

    Article  Google Scholar 

  12. Kallel F, Sahnoun M, Hamida AM, Chtourou K (2018) CT scan contrast enhancement using singular value decomposition and adaptive gamma correction. Signal Image Video Process 12(5):905–913

    Article  Google Scholar 

  13. Kanmani M, Narsimhan V (2018) An image contrast enhancement algorithm for grayscale images using particle swarm optimization. Multimed Tools Appl 77 (18):23371–23387

    Article  Google Scholar 

  14. Kim Y (1997) Contrast enhancement using brightness preserving Bi histogram equalization. IEEE Trans Consum Electron 43(1):1–8

    Article  Google Scholar 

  15. Lee E, Kim S, Kang W, Seo D, Paik J (2013) Contrast enhancement using dominant brightness level analysis and adaptive intensity transformation for remote sensing images. IEEE Geosci Remote Sens Lett 10(1):62–66

    Article  Google Scholar 

  16. Liu J, Zhou C, Chen P, Kang C (2017) An efficient contrast enhancement method for remote sensing images. IEEE Geosci Remote Sens Lett 14(10):1715–1719

    Article  Google Scholar 

  17. Mittal A, Soundararajan R, Bovik AC (2013) Making a completely blind image quality analyzer. IEEE Signal Process Lett 20(3):209–212

    Article  Google Scholar 

  18. Parihar AS, Verma OP (2016) Contrast enhancement using entropy-based dynamic sub-histogram equalization. IET Image Process 10(11):799–808

    Article  Google Scholar 

  19. Rahman S, Rahman MM, Wadud MAA, Quaderi GDA, Shoyaib M (2016) An adaptive gamma correction for image enhancement. EURASIP J Image Video Process 2016(35):1–13

    Google Scholar 

  20. Singh H, Kumar A, Balyan LK, Singh GK (2017) A novel optimally weighted framework of piecewise gamma corrected fractional order masking for satellite image enhancement. Comput Electr Eng, 1–17

  21. Singh H, Kumar A, Balyan LK, Singh GK (2018) Slantlet filter-bank based satellite image enhancement using gamma corrected knee transformation. Int J Electron 105(10):1695–1715

    Google Scholar 

  22. Suresh S, Lal S, Reddy CS, Kiran MS (2017) A novel adaptive cuckoo search algorithm for contrast enhancement of satellite images. IEEE J Selected Topics Appl Earth Observ Remote Sens 10(8):3665–3676

    Article  Google Scholar 

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Acknowledgements

This work was supported by Dept. of Electronics and Communication Engineering, NIT Delhi.

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Authors and Affiliations

  1. National Institute of Technology, Delhi, 110040, India

    Shubhi Kansal & Rajiv Kumar Tripathi

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  1. Shubhi Kansal
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  2. Rajiv Kumar Tripathi
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Correspondence to Shubhi Kansal.

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Kansal, S., Tripathi, R.K. Adaptive gamma correction for contrast enhancement of remote sensing images. Multimed Tools Appl 78, 25241–25258 (2019). https://doi.org/10.1007/s11042-019-07744-5

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  • DOI: https://doi.org/10.1007/s11042-019-07744-5

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