Image inverse halftoning and descreening: a review

  • Fan Zhang
  • Xinhong ZhangEmail author


With the development of computer technology and network technology, a large amount of printed documents are converted to the electronic documents and spread on internet. When a halftone image is scanned to an electronic document, the screen patterns will appear, so the inverse halftoning algorithms are needed to remove the screen patterns and improve image quality. In this paper, the halftoning techniques are introduced firstly, then this paper reviews different inverse halftoning algorithms. The inverse halftoning algorithms introduced in this paper include the low-pass filter algorithm, the fast algorithm, the wavelet based algorithm, the maximum posteriori probability algorithm, the LUT algorithm, the vector based algorithm, and the deconvolution based inverse halftoning algorithm. The image quality evaluation of these inverse halftoning algorithms is also discussed. Finally this paper summarizes the shortcomings of current inverse halftoning algorithms and the directions that can be improved in the future.


Digital halftoning Inverse halftoning Descreening Image quality evaluation 



This research was supported by the National Key Technology Research and Development Program of China (Grant No. 2015BAK01B06), the Natural Science Foundation of China (Grant No. 61771006, and No. U1504621) and the Natural Science Foundation of Henan Province (Grant No. 162300410032).


  1. 1.
    Achanta R, Shaji A, Smith K, Lucchi A, Fua P, SuSstrunk S (2012) Slic superpixels compared to state-of-the-art superpixel methods. IEEE Trans Pattern Anal Mach Intell 34(11):2274–2282Google Scholar
  2. 2.
    Au OC (2002) Fast ad-hoc inverse halftoning using adaptive filtering. In: IEEE international conference on acoustics, speech, and signal processing, pp 3461–3464Google Scholar
  3. 3.
    Burger M, Dirks H, Muller J (1984) Inverse problems in imaging. Am J Gastroenterol 79(11):861–863zbMATHGoogle Scholar
  4. 4.
    Chang PC, Yu CS, Lee TH (2001) Hybrid lms-mmse inverse halftoning technique. IEEE Trans Image Process 10(1):95–103zbMATHGoogle Scholar
  5. 5.
    Cheung S, Ulichney RA (1998) Low-memory low-complexity inverse dithering. Electron Imaging 3648(1):1–12Google Scholar
  6. 6.
    Chung KL, Wu ST (2005) Inverse halftoning algorithm using edge-based lookup table approach. IEEE Trans Image Process 14(10):1583–1589Google Scholar
  7. 7.
    Contributors W (2018) Dither., [Online]
  8. 8.
    Dameravenkata N, Kite TD, Venkataraman M, Evans BL (1998) Fast blind inverse halftoning. In: International conference on image processing, vol 2, pp 64–68Google Scholar
  9. 9.
    Damera-Venkata N, Kite TD, Geisler WS, Evans BL, Bovik AC (2000) Image quality assessment based on a degradation model. IEEE Trans Image Process 9 (4):636–650Google Scholar
  10. 10.
    Djebbouri M, Djebouri D, Naoum R (2005) Wavelet-based inverse halftoning for error diffused halftones. AEU - Int J Electron Commun 59(2):128–133Google Scholar
  11. 11.
    Eschbach R (1999) Optimal parallel error diffusion dithering. Proc SPIE - Int Soc Opt Eng 3648:485–494Google Scholar
  12. 12.
    Fan Z, Eschbach R (1994) Limit cycle behavior of error diffusion. In: IEEE international conference on image processing, vol 2, pp 1041–1045Google Scholar
  13. 13.
    Ferris LW (2002) A volumetric method for the detection and estimation of neutralizers in butter and in certain allied products. IEEE Trans Image Process 9(4):666–682Google Scholar
  14. 14.
    Floyd RW (1975) An adaptive algorithm for spatial grey scale. Sid Digest 17:75–77Google Scholar
  15. 15.
    Foi A, Katkovnik V, Egiazarian K, Astola J (2004) Inverse halftoning based on the anisotropic lpa-ici deconvolution. In: Proceedings of the international TICSP workshop spectral method multirate signal processing (SMMSP), pp 49–56Google Scholar
  16. 16.
    Freitas PG, Farias MCQ, Araujo APF (2016) Enhancing inverse halftoning via coupled dictionary training. Signal Process Image Commun 49:1–8Google Scholar
  17. 17.
    Glenn WE, Glenn KG, Bastian CJ (1985) Imaging system design based on psychophysical data. In: Proceedings of the Society for Information Display, vol 26, pp 71–78Google Scholar
  18. 18.
    He Z, Bouman CA (2004) Am/fm halftoning: digital halftoning through simultaneous modulation of dot size and dot density. J Electron Imaging 13(2):286–302Google Scholar
  19. 19.
    Hein S, Zakhor A (1995) Halftone to continuous-tone conversion of error-diffusion coded images. IEEE Trans Image Process 4(2):208–216Google Scholar
  20. 20.
    Hore A, Ziou D (2010) Image quality metrics: Psnr vs. ssim. In: International conference on pattern recognitionGoogle Scholar
  21. 21.
    Hou J, Gao H, Li X (2016) Dsets-dbscan: a parameter-free clustering algorithm. IEEE Trans Image Process 25(7):3182–3193MathSciNetGoogle Scholar
  22. 22.
    Jarvis JF, Judice CN, Ninke WH (1976) A survey of techniques for the display of continuous tone pictures on bilevel displays. Comput Graphics Image Process 5(1):13–40Google Scholar
  23. 23.
    Jimenez FP, Miyatake MN, Medina KT, Perez GS, Meana HP (2017) An inverse halftoning algorithms based on neural networks and atomic functions. IEEE Lat Am Trans 15(3):488–495Google Scholar
  24. 24.
    Johansson K, Lundberg P, Ryberg R (2011) A guide to graphic print production. Wiley, New YorkGoogle Scholar
  25. 25.
    Katkovnik V, Egiazarian K, Astola J (2003) Adaptive varying scale methods in image processing. In: Tampere international center for signal processing, vol 19, pp 21–28Google Scholar
  26. 26.
    Katkovnik V, Foi A, Egiazarian K, Astola J (2004) Directional varying scale approximations for anisotropic signal processing. In: European signal processing conference, pp 101–104Google Scholar
  27. 27.
    Kern RS, Stockham TG Jr, Strong DC (1993) Descreening linear and iterative filtering techniques. In: Human vision, visual processing, and digital display IV, pp 299–300Google Scholar
  28. 28.
    Kite TD, Evans BL, Bovik AC, Sculley TL (1997) Digital halftoning as 2-d delta-sigma modulation. In: International conference on image processing, pp 799–802Google Scholar
  29. 29.
    Kite TD, Damera-Venkata N, Evans BL, Bovik AC (2000) A fast, high-quality inverse halftoning algorithm for error diffused halftones. IEEE Trans Image Process 9(9):1583–1592Google Scholar
  30. 30.
    Knuth DE (1987) Digital halftones by dot diffusion. ACM Trans Graph 6(4):245–273MathSciNetzbMATHGoogle Scholar
  31. 31.
    Kong Y (2004) Inverse halftoning for error diffusion based on pattern recognition and look-up table. Chin J Sci Instrum 25(4):177–181MathSciNetGoogle Scholar
  32. 32.
    Lan X, Ma AJ, Yuen PC (2014) Multi-cue visual tracking using robust feature-level fusion based on joint sparse representation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1194–1201Google Scholar
  33. 33.
    Lan X, Ma A, Yuen PC, Chellappa R (2015) Joint sparse representation and robust feature-level fusion for multi-cue visual tracking. IEEE Trans Image Process 24(12):5826–5841MathSciNetGoogle Scholar
  34. 34.
    Lan X, Zhang S, Yuen PC, Chellappa R (2018) Learning common and feature-specific patterns: a novel multiple-sparse-representation-based tracker. IEEE Trans Image Process 27(4):2022–2037MathSciNetGoogle Scholar
  35. 35.
    Lau DL, Arce GR (2000) Digital halftoning. In: Nonlinear image processing, pp 375–401Google Scholar
  36. 36.
    Lau DL, Arce G (2001) Modern digital halftoning. Marcel Dekker Inc, New YorkGoogle Scholar
  37. 37.
    Marcu GG (2001) New methods for digital halftoning and inverse halftoning. Proc SPIE 4663:278–292Google Scholar
  38. 38.
    Mese M (2000) Template selection for lut inverse halftoning and application to color halftones. In: IEEE international conference on acoustics, speech, and signal processing, vol 4, pp 2290–2293Google Scholar
  39. 39.
    Mese M, Vaidyanathan PP (2001) Look-up table (lut) method for inverse halftoning. IEEE Trans Image Process 10(10):1566–1578Google Scholar
  40. 40.
    Mese M, Vaidyanathan PP (2002) Recent advances in digital halftoning and inverse halftoning methods. IEEE Trans Circ Syst I Fund Theory Appl 49(6):790–805Google Scholar
  41. 41.
    Miceli CM, Parker KJ (1992) Inverse halftoning. J Electron Imaging 1(2):143–151Google Scholar
  42. 42.
    Molinagarcia J, Ponomaryov VI, Reyesreyes R, Cruzramos C (2017) Parallel halftoning technique using dot diffusion optimization. Proc SPIE 223:102230KGoogle Scholar
  43. 43.
    Neelamani R, Nowak R, Baraniuk R (2002) Model-based inverse halftoning with wavelet-vaguelette deconvolution, vol 3. International conference on image processing, pp 973–976Google Scholar
  44. 44.
    Ostromoukhov V, Hersch RD, Amidror I (1994) Rotated dispersed dither: a new technique for digital halftoning. ACM, New York, pp 123–130Google Scholar
  45. 45.
    Pappas TN, Allebach JP, Neuhoff DL (2016) Model-based digital halftoning. Signal Process Mag IEEE 20(4):14–27Google Scholar
  46. 46.
    Safonov IV, Kurilin IV, Rychagov MN, Tolstaya EV (2018) Descreening of scanned images. SPIE PressGoogle Scholar
  47. 47.
    Sharma G, Bala R (2002) Digital color imaging handbook. J Mol Biol 33(1):109–1122Google Scholar
  48. 48.
    Shen J (2009) Least-squares halftoning via human vision system and markov gradient descent (ls-mgd): algorithm and analysis. SIAM Rev 51(3):567–589MathSciNetzbMATHGoogle Scholar
  49. 49.
    Shen MY, Kuo CCJ (2001) A robust nonlinear filtering approach to inverse halftoning. J Vis Commun Image Represent 12(1):84–95Google Scholar
  50. 50.
    Son CH (2012) Inverse halftoning based on sparse representation. Opt Lett 37(12):2352–2354Google Scholar
  51. 51.
    Son CH, Park HM (2012) Sparsity-based inverse halftoning. Electron Lett 48(14):832–834Google Scholar
  52. 52.
    Stevenson RL (1997) Inverse halftoning via map estimation. IEEE Trans Image Process 6(4):574–583Google Scholar
  53. 53.
    Sun B, Li S, Sun J (2014) Scanned image descreening with image redundancy and adaptive filtering. IEEE Trans Image Process 23(8):3698–3704MathSciNetzbMATHGoogle Scholar
  54. 54.
    Tang L, Ni J, Wang C, Zhang R (2008) A modified kernels-alternated error diffusion watermarking algorithm for halftone images. In: International workshop on digital watermarking, pp 382–394Google Scholar
  55. 55.
    Ulichney R A (1993) Void-and-cluster method for dither array generation. In: SPIE symposium on electronic imaging science and technology, Proceedings of SPIE conference on human vision, visual processing and digital display IV, pp 332–343Google Scholar
  56. 56.
    Velho L, Gomes JDM (1991) Digital halftoning with space filling curves. In: Conference on computer graphics and interactive techniques, pp 81–90Google Scholar
  57. 57.
    Weihua O, You X, Tao D, Zhang P, Tang Y, Zhu Z (2014) Robust face recognition via occlusion dictionary learning. Pattern Recognit 47(4):1559–1572Google Scholar
  58. 58.
    Wilson R (2009) File:halftoningcolor.svg., [Online]
  59. 59.
    Wong PW (1995) Inverse halftoning and kernel estimation for error diffusion. IEEE Trans Image Process 4(4):486–498MathSciNetGoogle Scholar
  60. 60.
    Xinge Y, Liang D, Yiu-Ming C, Qiuhui C (2010) A blind watermarking scheme using new nontensor product wavelet filter banks. IEEE Trans Image Process 19(12):3271–3284MathSciNetzbMATHGoogle Scholar
  61. 61.
    Xiong Z, Ramchandran K (1997) Wavelet-based approach to inverse halftoning. Proc SPIE 3018:89–100Google Scholar
  62. 62.
    Xiong Z, Orchard MT, Ramchandran K (1999) Inverse halftoning using wavelets. IEEE Trans Image Process 8(10):1479Google Scholar
  63. 63.
    Yi Y, Li R, Yu C, Yuan Y (2017) Quality evaluation metric for greyscale error diffusion halftone images based on texture and visual characteristics. Imaging Sci J 2017(1):1–12Google Scholar
  64. 64.
    Yu-jin Z (2012) Image engineering, 3rd edn. Beijing Tsinghua University Press, BeijingGoogle Scholar
  65. 65.
    Zhang C, Pan J, Chen S, Wang T, Sun D (2016) No reference image quality assessment using sparse feature representation in two dimensions spatial correlation. Neurocomputing 173:462–470Google Scholar
  66. 66.
    Zhang F, Qu X, Zhang R, Li Z, Zhang X (2017) Descreening algorithm of scanned images based on superpixel segmentation. Packag Eng 38(9):236–239Google Scholar
  67. 67.
    Zhang F, Li Z, Qu X, Zhang X (2018) Inverse halftoning algorithm based on slic superpixels and dbscan clustering. In: Lecture notes in artificial intelligence, vol 10956, pp 466–471Google Scholar
  68. 68.
    Zhong C, Miao D, Wang R (2010) A graph-theoretical clustering method based on two rounds of minimum spanning trees. Pattern Recognit 43(3):752–766zbMATHGoogle Scholar

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

Authors and Affiliations

  1. 1.School of Computer and Information EngineeringHenan UniversityKaifengChina
  2. 2.School of SoftwareHenan UniversityKaifengChina

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