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A comprehensive survey on image enhancement techniques with special emphasis on infrared images

  • 1197: Advances in Soft Computing Techniques for Visual Information-based Systems
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Abstract

Capturing of infrared images is an easy task but perceptual visualization is difficult due to environmental conditions such as light rain, partly cloudy, mostly cloudy, haze, poor lightening conditions, noise generated by the sensors, geographical distance and appearances of the objects. To improve the human perception and quality of the infrared images for further processing like image analysis, image enhancement is an essential process. This paper provides a detailed review of various image enhancement techniques from contrast stretching to optimization methods used in infrared images. It also discusses the existing infrared image enhancement techniques as group such as histogram based methods, filter based methods, transform domain based methods, morphological based methods, saliency extraction methods, fuzzy based methods, learning methods, optimization methods and its popular algorithms also address the countless issues. Some of the existing image enhancement methods (Histogram Equlization, Max-median filter, Top-Hat transform) and infrared image enhancement methods (multi-scale top-hat transform, adaptive infrared image enhancement) are implemented along with the adaptive fuzzy based infrared image enhancement method and its obtained results evaluation is done on subjective and objective ways. From the results observed that the fuzzy based method works well for both subjective and objective evaluation. The paper aims to provide a complete study on image enhancement techniques and how they specially utilized while dealing with infrared images. In addition, the paper helps the researchers to select the suitable infrared image enhancement techniques for their infrared image application needs.

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References

  1. Achanta R, Hemami S, Estrada F, Susstrunk S (2009) Frequency-tuned salient region detection. In: Computer vision and pattern recognition, 2009. cvpr 2009. IEEE conference on. IEEE, pp 1597–1604

  2. Ashiba HI, Awadalla KH, El-Halfawy SM, Abd El-Samie FES (2008) Homomorphic enhancement of infrared images using the additive wavelet transform. Prog Electromagn Res 1:123–130

    MATH  Google Scholar 

  3. Bai X (2014) Morphological center operator for enhancing small target obtained by infrared imaging sensor. Optik 125(14):3697–3701

    Google Scholar 

  4. Bai X, Chen X, Zhou F, Liu Z, Xue B (2013) Multiscale top-hat selection transform based infrared and visual image fusion with emphasis on extracting regions of interest. Infrared Phys Technol 60:81–93

    Google Scholar 

  5. Bai X, Zhou F (2010) Analysis of new top-hat transformation and the application for infrared dim small target detection. Pattern Recogn 43(6):2145–2156

    MATH  Google Scholar 

  6. Bai X, Zhou F (2010) Infrared small target enhancement and detection based on modified top-hat transformations. Comput Electric Eng 36(6):1193–1201

    Google Scholar 

  7. Bai X, Zhou F (2013) A unified form of multi-scale top-hat transform based algorithms for image processing. Optik-Int J Light Electron Opt 124 (13):1614–1619

    Google Scholar 

  8. Bai X, Zhou F, Jin T (2010) Enhancement of dim small target through modified top-hat transformation under the condition of heavy clutter. Signal Process 90(5):1643–1654

    MATH  Google Scholar 

  9. Bai X, Zhou F, Xue B (2011) Infrared image enhancement through contrast enhancement by using multiscale new top-hat transform. Infrared Phys Technol 54(2):61–69

    Google Scholar 

  10. Chen SD, Ramli AR (2003) Minimum mean brightness error bi-histogram equalization in contrast enhancement. IEEE Trans Consum Electron 49 (4):1310–1319

    Google Scholar 

  11. 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

    Google Scholar 

  12. Davis JW, Sharma V (2007) Otcbvs benchmark dataset collection

  13. Deshpande SD, Meng HE, Venkateswarlu R, Chan P (1999) Max-mean and max-median filters for detection of small targets. In: SPIE’s International symposium on optical science, engineering, and instrumentation. International Society for Optics and Photonics, pp 74–83

  14. Dougherty ER, Lotufo RA (2003) for Optical Engineering SPIE, T.I.S.: Hands-on morphological image processing, vol 71. SPIE press, Bellingham

    Google Scholar 

  15. Ein-shoka AA, Faragallah OS (2018) Quality enhancement of infrared images using dynamic fuzzy histogram equalization and high pass adaptation in DWT. Optik 160:146–158

    Google Scholar 

  16. Fan Z, Bi D, Ding W (2017) Infrared image enhancement with learned features. Infrared Phys Technol 86:44–51

    Google Scholar 

  17. Gonzalez RC, Woods RE (2002) Digital image processing

  18. Gupta KK, Beg R, Niranjan JK (2012) A novel approach to fast image filtering algorithm of infrared images based on intro sort algorithm. arXiv:1201.3972

  19. Hadhoud MM, Thomas DW (1988) The two-dimensional adaptive lms (tdlms) algorithm. IEEE Trans Circ Syst 35(5):485–494

    Google Scholar 

  20. Haralock RM, Shapiro LG (1991) Computer and robot vision, Addison-Wesley Longman Publishing Co. Inc, Boston

  21. Haykin S, Widrow B (2003) Least-mean-square adaptive filters, vol 31. Wiley, New York

    Google Scholar 

  22. Horn B (1986) Robot vision, MIT Press, Cambridge

  23. Huang Z, Zhang T, Li Q, Fang H (2016) Adaptive gamma correction based on cumulative histogram for enhancing near-infrared images. Infrared Phys Technol 79:205–215

    Google Scholar 

  24. Jackway PT, Deriche M (1996) Scale-space properties of the multiscale morphological dilation-erosion. IEEE Trans Pattern Anal Mach Intell 18 (1):38–51

    Google Scholar 

  25. Karali AO, Okman OE, Aytac T (2011) Adaptive image enhancement based on clustering of wavelet coefficients for infrared sea surveillance systems. Infrared Phys Technol 54(5):382–394

    Google Scholar 

  26. Kim YT (1997) Contrast enhancement using brightness preserving bi-histogram equalization. IEEE Trans Consum Electron 43(1):1–8

    Google Scholar 

  27. Kuang X, Sui X, Liu Y, Chen Q, Gu G (2019) Single infrared image enhancement using a deep convolutional neural network. Neurocomputing 332:119–128

    Google Scholar 

  28. Lewis J (1995) Fast normalized cross-correlation. In: Vision interface, vol 10, pp 120–123. https://doi.org/10.1.1.21.6062

  29. Li S, Jin W, Li L, Li Y (2018) An improved contrast enhancement algorithm for infrared images based on adaptive double plateaus histogram equalization. Infrared Phys Technol 90(1):164–174

    Google Scholar 

  30. Li Y, Liu N, Xu J, Wu J (2019) Detail enhancement of infrared image based on bi-exponential edge preserving smoother. Optik 199:1–11

    Google Scholar 

  31. Lin CL (2011) An approach to adaptive infrared image enhancement for long-range surveillance. Infrared Phys Technol 54(2):84–91

    Google Scholar 

  32. Liu N, Chen X (2016) Infrared image detail enhancement approach based on improved joint bilateral filter. Infrared Phys Technol 77:405–413

    Google Scholar 

  33. Liu N, Zhao D (2014) Detail enhancement for high-dynamic-range infrared images based on guided image filter. Infrared Phys Technol 67:138–147

    Google Scholar 

  34. Matheron G, Serra J. (1982) Image analysis and mathematical morphology

  35. Menotti D, Najman L, Facon J, De Araujo A (2007) Multi-histogram equalization methods for contrast enhancement and brightness preserving. IEEE Trans Consum Electron 53(3):1186–1194

    Google Scholar 

  36. Mukhopadhyay S, Chanda B (2000) A multiscale morphological approach to local contrast enhancement. Signal Process 80(4):685–696

    MATH  Google Scholar 

  37. Oliveira M, Leite NJ (2008) A multiscale directional operator and morphological tools for reconnecting broken ridges in fingerprint images. Pattern Recogn 41(1):367–377

    MATH  Google Scholar 

  38. Patel S, Goswami M (2014) Comparative analysis of histogram equalization techniques. In: 2014 International conference on contemporary computing and informatics (IC3I). IEEE, pp 167–168

  39. Rajkumar S, Mouli PC (2015) Target detection in infrared images using block-based approach. In: Informatics and communication technologies for societal development. Springer, pp 9–16

  40. Roebuck K (2012) Terahertz radiation: high-impact emerging technology-what you need to know: definitions, adoptions, impact, benefits, maturity, vendors. Emereo Publishing, Aspley

    Google Scholar 

  41. Sayood K (2012) Introduction to data compression. Newnes, London

    MATH  Google Scholar 

  42. Schalko RJ (1989) Digital image processing and computer vision, vol 286. Wiley, New York

    Google Scholar 

  43. 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

    Google Scholar 

  44. Sim K, Tso C, Tan Y (2007) Recursive sub-image histogram equalization applied to gray scale images. Pattern Recogn Lett 28(10):1209–1221

    Google Scholar 

  45. Song YF, Shao XP, Xu J (2008) New enhancement algorithm for infrared image based on double plateaus histogram [j]. Infrared Laser Eng 2:1–29

    Google Scholar 

  46. Song Q, Wang Y, Bai K (2016) High dynamic range infrared images detail enhancement based onn local edge preserving filter. Infrared Phys Technol 77:464–473

    Google Scholar 

  47. Soni T, Rao BD, Zeidler JR, Ku WH (1991) Enhancement of images using the 2-d lms adaptive algorithm. In: 1991 International conference on acoustics, speech, and signal processing, 1991. ICASSP-91. IEEE, pp 3029–3032

  48. Soundrapandiyan R, PVSSR CM (2015) Perceptual visualization enhancement of infrared images using fuzzy sets. In: Transactions on computational science XXV. Springer, pp 1–17

  49. Vernon D (1991) Machine vision-automated visual inspection and robot vision

  50. Vickers VE (1996) Plateau equalization algorithm for real-time display of high-quality infrared imagery. Optical Eng 35(7):1921–1926

    Google Scholar 

  51. Wang Z, Bovik AC (2002) A universal image quality index. Signal Process Lett IEEE 9(3):81–84. https://doi.org/10.1109/97.995823

    Article  Google Scholar 

  52. Wang Z, Bovik AC, Sheikh HR, Simoncelli EP (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

    Article  Google Scholar 

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

    Google Scholar 

  54. Wang BJ, Liu SQ, Li Q, Zhou HX (2006) A real-time contrast enhancement algorithm for infrared images based on plateau histogram. Infrared Phys Technol 48(1):77–82

    Google Scholar 

  55. Xu F, Zeng D, Zhang J, Zheng Z, Wei F, Wang T (2016) Detail enhancement of blurred infrared images based on frequency extrapolation. Infrared Phys Technol 76:560–568

    Google Scholar 

  56. Zhao J, Chen Y, Feng H, Xu Z, Li Q (2014) Infrared image enhancement through saliency feature analysis based on multi-scale decomposition. Infrared Phys Technol 62:86–93

    Google Scholar 

  57. Zhao J, Chen Y, Feng H, Xu Z, Li Q (2014) Fast image enhancement using multi-scale saliency extraction in infrared imagery. Optik-Int J Light Electron Opt 125(15):4039–4042

    Google Scholar 

  58. Zhao J, Feng H, Xu Z, Li Q, Liu T (2013) Detail enhanced multi-source fusion using visual weight map extraction based on multi scale edge preserving decomposition. Opt Commun 287:45–52

    Google Scholar 

  59. Zhao J, Qu S (2011) The fuzzy nonlinear enhancement algorithm of infrared image based on curvelet transform. Procedia Eng 15:3754–3758

    Google Scholar 

  60. Zuiderveld K (1994) Contrast limited adaptive histogram equalization. In: Graphics gems IV. Academic Press Professional, Inc, Cambridge, pp 474–485

  61. Zhao F, Zhao J, Zhao W, Qu F (2016) Gaussian mixture model-based gradient field reconstruction for infrared image detail enhancement and denoising. Infrared Phys Technol 76:408–414

    Google Scholar 

  62. Zuo C, Chen Q, Sui X (2013) Range limited bi-histogram equalization for image contrast enhancement. Optik-Int J Light Electron Opt 124(5):425–431

    Google Scholar 

Download references

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Correspondence to Suresh Chandra Satapathy.

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Soundrapandiyan, R., Satapathy, S.C., P.V.S.S.R., C.M. et al. A comprehensive survey on image enhancement techniques with special emphasis on infrared images. Multimed Tools Appl 81, 9045–9077 (2022). https://doi.org/10.1007/s11042-021-11250-y

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