Skip to main content

Logarithmic Adaptive Neighborhood Image Processing (LANIP): Introduction, Connections to Human Brightness Perception, and Application Issues

Abstract

A new framework for image representation, processing, and analysis is introduced and exposed through practical applications. The proposed approach is called logarithmic adaptive neighborhood image processing (LANIP) since it is based on the logarithmic image processing (LIP) and on the general adaptive neighborhood image processing (GANIP) approaches, that allow several intensity and spatial properties of the human brightness perception to be mathematically modeled and operationalized, and computationally implemented. The LANIP approach is mathematically, computationally, and practically relevant and is particularly connected to several human visual laws and characteristics such as: intensity range inversion, saturation characteristic, Weber's and Fechner's laws, psychophysical contrast, spatial adaptivity, multiscale adaptivity, morphological symmetry property. The LANIP approach is finally exposed in several areas: image multiscale decomposition, image restoration, image segmentation, and image enhancement, through biomedical materials and visual imaging applications.

References

  1. 1.

    Stockham TG Jr.: Image processing in the context of a visual model. Proceedings of the IEEE 1972,60(7):828-842.

    Google Scholar 

  2. 2.

    Hunt BR: Digital image processing. Proceedings of the IEEE 1975,63(4):693-708.

    Google Scholar 

  3. 3.

    Kelly DH: Image processing experiments. Journal of the Optical Society of America 1961,51(10):1095-1101. 10.1364/JOSA.51.001095

    Google Scholar 

  4. 4.

    Jain AK: Advances in mathematical models for image processing. Proceedings of the IEEE 1981,69(5):502-528.

    Google Scholar 

  5. 5.

    Granrath DJ: The role of human visual models in image processing. Proceedings of the IEEE 1981,69(5):552-561.

    Google Scholar 

  6. 6.

    Marr D: Vision: A Computational Investigation into the Human Representation and Processing of Visual Information. W. H. Freeman, San Fransisco, Calif, USA; 1982.

    Google Scholar 

  7. 7.

    Myers DG: Digital Signal Processing Efficient Convolution and Fourier Transform Technique. Prentice-Hall, Upper Saddle River, NJ, USA; 1990.

    Google Scholar 

  8. 8.

    Schreiber WF: Fundamentals of Electronic Imaging Systems: Some Aspects of Image Processing. 2nd edition. Springer, Berlin, Germany; 1991.

    Google Scholar 

  9. 9.

    Gonzalez RC, Wintz P: Digital Image Processing. Addison-Wesley, Reading, Mass, USA; 1987.

    Google Scholar 

  10. 10.

    Pinoli J-C: A general comparative study of the multiplicative homomorphic, log-ratio and logarithmic image processing approaches. Signal Processing 1997,58(1):11-45. 10.1016/S0165-1684(97)00011-X

    MATH  Google Scholar 

  11. 11.

    Shen J: On the foundations of vision modeling. I. Weber's law and Weberized TV restoration. Physica D: Nonlinear Phenomena 2003,175(3-4):241-251. 10.1016/S0167-2789(02)00734-0

    MathSciNet  MATH  Google Scholar 

  12. 12.

    Shah M: Guest introduction: the changing shape of computer vision in the twenty-first century. International Journal of Computer Vision 2002,50(2):103-110. 10.1023/A:1020323930790

    Google Scholar 

  13. 13.

    Shen J: On the foundations of vision modeling. II. Mining of mirror symmetry of 2-D shapes. Journal of Visual Communication and Image Representation 2005,16(3):250-270. 10.1016/j.jvcir.2004.11.003

    Google Scholar 

  14. 14.

    Palmer SE: Theoretical approaches to vision. In Vision Science: Photons to Phenomenology. MIT Press, Cambridge, Mass, USA; 1999:45-92.

    Google Scholar 

  15. 15.

    Wertheimer M: Laws of organization in perceptual forms. In A Sourcebook of Gestalt Psychology. Hartcourt Brace, San Diego, Calif, USA; 1938:71-88.

    Google Scholar 

  16. 16.

    Kovács I: Gestalten of today: early processing of visual contours and surfaces. Behavioural Brain Research 1996,82(1):1-11. 10.1016/S0166-4328(97)81103-5

    Google Scholar 

  17. 17.

    Desolneux A, Moisan L, Morel J-M: Computational gestalts and perception thresholds. Journal of Physiology-Paris 2003,97(2-3):311-324. 10.1016/j.jphysparis.2003.09.006

    Google Scholar 

  18. 18.

    Xie Z, Stockham TG Jr.: Toward the unification of three visual laws and two visual models in brightness perception. IEEE Transactions on Systems, Man and Cybernetics 1989,19(2):379-387. 10.1109/21.31040

    Google Scholar 

  19. 19.

    Jourlin M, Pinoli J-C: Logarithmic image processing. Acta Stereologica 1987, 6: 651–656.

    Google Scholar 

  20. 20.

    Jourlin M, Pinoli J-C: A model for logarithmic image processing. Journal of Microscopy 1988, 149: 21–35. 10.1111/j.1365-2818.1988.tb04559.x

    Google Scholar 

  21. 21.

    Jourlin M, Pinoli J-C: Logarithmic image processing: the mathematical and physical framework for the representation and processing of transmitted images. Advances in Imaging and Electron Physics 2001, 115: 129–196.

    Google Scholar 

  22. 22.

    Pinoli J-C: The logarithmic image processing model: connections with human brightness perception and contrast estimators. Journal of Mathematical Imaging and Vision 1997,7(4):341-358. 10.1023/A:1008259212169

    Google Scholar 

  23. 23.

    Pinoli J-C: Contribution à la modélisation, au traitement et à l'analyse d'image, Ph.D. thesis. Department of Mathematics, University of Saint-Etienne, Saint-Etienne, France; February 1987.

    Google Scholar 

  24. 24.

    Mayet F, Pinoli J-C, Jourlin M: Justifications physiques et applications du modèle LIP pour le traitement des images obtenues en lumière transmise. Traitement du Signal 1996, 13: 251–262.

    MATH  Google Scholar 

  25. 25.

    Deng G: Image and signal processing using the logarithmic image processing model, Ph.D. thesis. Department of Electronic Engineering, La Trobe University, Melbourne, Australia; 1993.

    Google Scholar 

  26. 26.

    Deng G, Cahill LW: Image modelling and processing using the logarithmic image processing model. Proceedings of the IEEE Workshop on Visual Signal Processing and Communications, September 1993, Melbourne, Australia 61–64.

    Google Scholar 

  27. 27.

    Brailean JC, Little D, Giger ML, Chen C-T, Sullivan BJ: Application of the EM algorithm to radiographic images. Medical Physics 1992,19(5):1175-1182. 10.1118/1.596895

    Google Scholar 

  28. 28.

    Jourlin M, Pinoli J-C, Zeboudj R: Contrast definition and contour detection for logarithmic images. Journal of Microscopy 1989, 156: 33–40. 10.1111/j.1365-2818.1989.tb02904.x

    Google Scholar 

  29. 29.

    Arce GR, Foster RE: Detail-preserving ranked-order based filters for image processing. IEEE Transactions on Acoustics, Speech, and Signal Processing 1989,37(1):83-98. 10.1109/29.17503

    Google Scholar 

  30. 30.

    Nagao M, Matsuyama T: Edge preserving smoothing. Computer Graphics and Image Processing 1979,9(4):394-407. 10.1016/0146-664X(79)90102-3

    Google Scholar 

  31. 31.

    Song W-J, Pearlman WA: Restoration of noisy images with adaptive windowing and nonlinear filtering. Visual Communications and Image Processing, 1986, Cambridge, Mass, USA, Proceedings of SPIE 707: 198–206.

    Google Scholar 

  32. 32.

    Salembier P: Structuring element adaptation for morphological filters. Journal of Visual Communication and Image Representation 1992,3(2):115-136. 10.1016/1047-3203(92)90010-Q

    Google Scholar 

  33. 33.

    Vogt RC: A spatially variant, locally adaptive, background normalization operator. In Mathematical Morphology and Its Applications to Image Processing. Edited by: Serra J, Soille P. Kluwer Academic, Dordrecht, The Netherlands; 1994:45-52.

    Google Scholar 

  34. 34.

    Debayle J, Pinoli J-C: General adaptive neighborhood image processing: Part I: introduction and theoretical aspects. Journal of Mathematical Imaging and Vision 2006,25(2):245-266. 10.1007/s10851-006-7451-8

    MathSciNet  Google Scholar 

  35. 35.

    Debayle J: General adaptive neighborhood image processing, Ph.D. thesis. Ecole Nationale Supérieure des Mines, Saint-Etienne, France; November 2005.

    Google Scholar 

  36. 36.

    Debayle J, Pinoli J-C: Spatially adaptive morphological image filtering using intrinsic structuring elements. Image Analysis and Stereology 2005,24(3):145-158. 10.5566/ias.v24.p145-158

    MathSciNet  MATH  Google Scholar 

  37. 37.

    Oppenheim AV: Generalized superposition. Information and Control 1967,11(5-6):528-536. 10.1016/S0019-9958(67)90739-5

    MATH  Google Scholar 

  38. 38.

    Oppenheim AV: Superposition in a class of nonlinear systems. Research Laboratory of Electronics, MIT, Cambridge, Mass, USA; 1965.

    Google Scholar 

  39. 39.

    Debayle J, Pinoli J-C: Adaptive-neighborhood mathematical morphology and its applications to image filtering and segmentation. Proceedings of the 9th European Congress on Stereology and Image Analysis (ECSIA '05), May 2005, Zakopane, Poland 2: 123–130.

    Google Scholar 

  40. 40.

    Debayle J, Pinoli J-C: Multiscale image filtering and segmentation by means of adaptive neighborhood mathematical morphology. Proceedings of the IEEE International Conference on Image Processing (ICIP '05), September 2005, Genova, Italy 3: 537–540.

    Google Scholar 

  41. 41.

    Debayle J, Pinoli J-C: General adaptive neighborhood image processing: Part II: practical application examples. Journal of Mathematical Imaging and Vision 2006,25(2):267-284. 10.1007/s10851-006-7452-7

    MathSciNet  Google Scholar 

  42. 42.

    Dainty JC, Shaw R: Image Science. Academic Press, New York, NY, USA; 1974.

    Google Scholar 

  43. 43.

    Born M, Wolf E: Principle of Optics. Cambridge University Press, New York, NY, USA; 1999.

    Google Scholar 

  44. 44.

    Atkins PW: Physical Chemistry. 5th edition. Oxford University Press, Oxford, UK; 1994.

    Google Scholar 

  45. 45.

    Gordon IE: Theories of Visual Perception. Psychology Press, New York, NY, USA; 2004.

    Google Scholar 

  46. 46.

    Watt R: Understanding Vision. Academic Press, San Diego, Calif, USA; 1991.

    Google Scholar 

  47. 47.

    Mather G: Foundations of Perception. Psychology Press, New York, NY, USA; 2006.

    Google Scholar 

  48. 48.

    Jain AK: Fundamentals of Digital Image Processing. Prentice-Hall, Englewood Cliffs, NJ, USA; 1989.

    Google Scholar 

  49. 49.

    Pratt WK: Digital Image Processing. 2nd edition. John Wiley & Sons, New York, NY, USA; 1991.

    Google Scholar 

  50. 50.

    Palomares JM, González J, Ros E: Designing a fast convolution under the LIP paradigm applied to edge detection. In Proceedings of the 3rd International Conference on Advances in Pattern Recognition (ICAPR '05), August 2005, Bayh, UK, Lecture Notes in Computer Science Edited by: Singh S, Singh M, Apte C, Perner P. 3687: 560–569.

    Google Scholar 

  51. 51.

    Pinoli J-C: Metrics, scalar product and correlation adapted to logarithmic images. Acta Stereologica 1992, 11: 157–168.

    Google Scholar 

  52. 52.

    Courbebaisse G, Trunde F, Jourlin M: Wavalet transform and LIP model. Image Analysis and Stereology 2002, 21: 121–125. 10.5566/ias.v21.p121-125

    Google Scholar 

  53. 53.

    Deng G, Cahill LW: The logarithmic image processing model and its applications. Proceedings of the 27th Asilomar Conference on Signals, Systems and Computers (ACSSC '93), November 1993, Pacific Grove, Calif, USA 2: 1047–1051.

    Google Scholar 

  54. 54.

    Wu Q-Z, Jeng B-S: Background subtraction based on logarithmic intensities. Pattern Recognition Letters 2002,23(13):1529-1536. 10.1016/S0167-8655(02)00116-2

    MATH  Google Scholar 

  55. 55.

    Bron C, Gremillet P, Launey D, et al.: Three-dimensional electron microscopy of entire cells. Journal of Microscopy 1990, 157, part 1: 115–126.

    Google Scholar 

  56. 56.

    Hautière N, Labayrade R, Aubert D: Detection of visibility conditions through use of on board cameras. Proceedings of IEEE Intelligent Vehicles Symposium (IVS '05), June 2005, Las Vegas, Nev, USA 193–198.

    Google Scholar 

  57. 57.

    Deng G, Cahill LW: Multiscale image enhancement using the logarithmic image processing model. Electronics Letters 1993,29(9):803-804. 10.1049/el:19930536

    Google Scholar 

  58. 58.

    Deng G, Cahill LW, Tobin GR: A study of logarithmic image processing model and its application to image enhancement. IEEE Transactions on Image Processing 1995,4(4):506-512. 10.1109/83.370681

    Google Scholar 

  59. 59.

    Jourlin M, Pinoli J-C: Image dynamic range enhancement and stabilization in the context of the logarithmic image processing model. Signal Processing 1995,41(2):225-237. 10.1016/0165-1684(94)00102-6

    MATH  Google Scholar 

  60. 60.

    Ramponi G: A cubic unsharp masking technique for contrast enhancement. Signal Processing 1998,67(2):211–222. 10.1016/S0165-1684(98)00038-3

    MATH  Google Scholar 

  61. 61.

    Chang D-C, Wu W-R: Image contrast enhancement based on a histogram transformation of local standard deviation. IEEE Transactions on Medical Imaging 1998,17(4):518-531. 10.1109/42.730397

    MathSciNet  Google Scholar 

  62. 62.

    Mishra N, Kumar PS, Chandrakanth R, Ramachandran R: Image enhancement using logarithmic image processing model. IETE Journal of Research 2000,46(5):309-313.

    Google Scholar 

  63. 63.

    Corcuff P, Gremillet P, Jourlin M, Duvault Y, Leroy F, Leveque JL: 3D reconstruction of human air by confocal microscopy. Journal of the Society of Cosmetic Chemists 1993, 44: 1–12.

    Google Scholar 

  64. 64.

    Gremillet P, Jourlin M, Pinoli J-C: LIP-model-based three-dimensionnal reconstruction and visualisation of HIV infected entire cells. Journal of Microscopy 1994,174(1):31-38. 10.1111/j.1365-2818.1994.tb04321.x

    Google Scholar 

  65. 65.

    Deng G, Cahill LW: Contrast edge detection using the logarithmic image processing model. Proceedings of the International Conference on Signal Processing, October 1993, Beijing, China 792–796.

    Google Scholar 

  66. 66.

    Roux B, Faure RM: Recognition and quantification of clinker phases by image analysis. Acta Stereologica 1992, 11: 149–154.

    Google Scholar 

  67. 67.

    Brailean JC, Sullivan BJ, Chen C-T, Giger ML: Evaluating the EM algorithm for image processing using a human visual fidelity criterion. Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP '91), May 1991, Toronto, Ont, Canada 4: 2957–2960.

    Google Scholar 

  68. 68.

    Deng G, Cahill LW: A novel nonlinear image filtering algorithm using the logarithmic image processing model. Proceedings of the 8th IEEE Workshop on Image and Multidimensional Signal Processing, September 1993, Cannes, France 61–64.

    Google Scholar 

  69. 69.

    Jourlin M, Montard N: A logarithmic version of the top-hat transform in connection with the Asplund distance. Acta Stereologica 1998,16(3):201-208.

    MATH  Google Scholar 

  70. 70.

    Roux B: Mise au point d'une méthode d'analyse d'images qui reconnaît et quantifie les phases de clinker, Ph.D. thesis. University of Saint-Etienne, Saint-Etienne, France; 1993.

    Google Scholar 

  71. 71.

    Comaniciu D: LIP-based edge block detector for classified vector quantization. Revue Roumaine des Sciences Techniques Serie Electrotechnique et Energetique 1996,41(1):89-102.

    Google Scholar 

  72. 72.

    Hou ZJ, Wei GW: A new approach to edge detection. Pattern Recognition 2002,35(7):1559-1570. 10.1016/S0031-3203(01)00147-9

    MATH  Google Scholar 

  73. 73.

    Deng G, Cahill LW: The contrast pyramid using the logarithmic image processing model. Proceedings of the 2nd International Conference on Simulation and Modelling, July 1993, Melbourne, Australia 75–82.

    Google Scholar 

  74. 74.

    Metzler VH, Lehmann TM, Aach T: Morphological multiscale shape analysis of light micrographs. Nonlinear Image Processing XI, January 2000, San Jose, Calif, USA, Proceedings of SPIE 3961: 227–238.

    Google Scholar 

  75. 75.

    Deng G, Cahill LW: Generating sketch image for very low bit rate image communication. Proceedings of the 1st IEEE Australian and New Zealand Conference on Intelligent Information Systems (ANZIIS '93), December 1993, Perth, Australia 407–411.

    Google Scholar 

  76. 76.

    Deng G, Cahill LW: Low-bit-rate image coding using sketch image and JBIG. Still-Image Compression, February 1995, San Jose, Calif, USA, Proceedings of SPIE 2418: 212–220.

    Google Scholar 

  77. 77.

    Luthon F, Caplier A, Liévin M: Spatiotemporal MRF approach to video segmentation: application to motion detection and lip segmentation. Signal Processing 1999,76(1):61-80. 10.1016/S0165-1684(98)00247-3

    MATH  Google Scholar 

  78. 78.

    Liévin M, Luthon F: Nonlinear color space and spatiotemporal MRF for hierarchical segmentation of face features in video. IEEE Transactions on Image Processing 2004,13(1):63-71. 10.1109/TIP.2003.818013

    Google Scholar 

  79. 79.

    Patrascu V, Buzuloiu V: Color image enhancement in the framework of logarithmic models. Proceedings of the 8th IEEE International Conference on Telecommunications, June 2001, Bucharest, Romania 1: 199–204.

    Google Scholar 

  80. 80.

    Oppenheim AV, Schafer RW, Stockham TG Jr.: Nonlinear filtering of multiplied and convolved signals. Proceedings of the IEEE 1968,56(8):1264–1291.

    Google Scholar 

  81. 81.

    Oppenheim AV, Schafer RW: Digital Signal Processing. Prentice-Hall, Englewood Cliffs, NJ, USA; 1975.

    Google Scholar 

  82. 82.

    Harouche E, Peleg S, Shvaytser H, Davis LS: Noisy image restoration by cost function minimization. Pattern Recognition Letters 1985,3(1):65-69. 10.1016/0167-8655(85)90044-3

    Google Scholar 

  83. 83.

    Shvayster H, Peleg S: Pictures as elements in a vector space. Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition (CVPR '83), June 1983, Washington, DC, USA 442–446.

    Google Scholar 

  84. 84.

    Shvayster H, Peleg S: Inversion of picture operators. Pattern Recognition Letters 1987,5(1):49–61. 10.1016/0167-8655(87)90025-0

    Google Scholar 

  85. 85.

    Brailean JC, Little D, Giger ML, Chen C-T, Sullivan BJ: Quantitative performance evaluation of the EM algorithm applied to radiographic images. Biomedical Image Processing II, February 1991, San Diego, Calif, USA, Proceedings of SPIE 1450: 40–46.

    Google Scholar 

  86. 86.

    Pinoli J-C: A contrast definition for logarithmic images in the continuous setting. Acta Stereologica 1991, 10: 85–96.

    MATH  Google Scholar 

  87. 87.

    Pirenne MH: Vision and the Eye. 2nd edition. Associated Book, New York, NY, USA; 1967.

    Google Scholar 

  88. 88.

    Zuidema P, Koenderink JJ, Bouman MA: A mechanistic approach to threshold behavior of the visual system. IEEE Transactions on Systems, Man and Cybernetics 1983,13(5):923-934.

    Google Scholar 

  89. 89.

    Baylor DA, Lamb TD, Yau K-W: Responses of retinal rods to single photons. Journal of Physiology 1979,288(1):613-634.

    Google Scholar 

  90. 90.

    Baylor DA, Nunn BJ, Schnapf JL: The photo-current, noise and spectral sensitivity of rods of the monkey Macaca Fascicularis . Journal of Physiology 1984,357(1):575-607.

    Google Scholar 

  91. 91.

    Weber EH: Der tastsinn und das gemeingefühl. In Handwörterbuch der Physiologie. Volume 3. Edited by: Wagner E. Friedrich Vieweg & Sohn, Braunschweig, Germany; 1846:481-588.

    Google Scholar 

  92. 92.

    Stevens SS: Handbook of Experimental Psychology. John Wiley & Sons, New York, NY, USA; 1951.

    Google Scholar 

  93. 93.

    Blackwell HR: Contrast thresholds of the human eye. Journal of the Optical Society of America 1946, 36: 624–643. 10.1364/JOSA.36.000624

    Google Scholar 

  94. 94.

    Lamar ES, Hecht S, Shlaer S, Hendlay D: Size, shape, and contrast in detection of targets by daylight vision. Journal of the Optical Society of America 1947,37(7):531-545. 10.1364/JOSA.37.000531

    Google Scholar 

  95. 95.

    Buchsbaum G: An analytical derivation of visual nonlinearity. IEEE Transactions on Biomedical Engineering 1980,27(5):237-242.

    Google Scholar 

  96. 96.

    Krueger LE: Reconciling Fechner and Stevens: toward a unified psychophysical law. Behavioral and Brain Sciences 1989,12(2):251-320. 10.1017/S0140525X0004855X

    Google Scholar 

  97. 97.

    Fechner GT: Elements of Psychophysics. Vol. 1. Holt, Rinehart & Winston, New York, NY, USA; 1960. English translation by H. E. Adler

    Google Scholar 

  98. 98.

    Fuortes MGF: Initiation of impulses in visual cells of Limulus. Journal of Physiology 1959,148(1):14-28.

    Google Scholar 

  99. 99.

    De Vries H: The quantum character of light and its bearing upon threshold of vision, the differential sensitivity and visual acuity of the eye. Physica 1943,10(7):553-564. 10.1016/S0031-8914(43)90575-0

    Google Scholar 

  100. 100.

    Rose A: The sensitivity performance of the human eye on an absolute scale. Journal of the Optical Society of America 1948,38(2):196-208. 10.1364/JOSA.38.000196

    Google Scholar 

  101. 101.

    Rose A: Vision: Human and Electronic. Plenum Press, New York, NY, USA; 1973.

    Google Scholar 

  102. 102.

    Zeevi YY, Mangoubi SS: Noise suppression in photoreceptors and its relevance to incremental intensity thresholds. Journal of the Optical Society of America 1978,68(12):1772-1776. 10.1364/JOSA.68.001772

    Google Scholar 

  103. 103.

    Stevens SS: On the psychophysical law. Psychological Review 1957,64(3):153-181.

    Google Scholar 

  104. 104.

    Stevens SS, Galanter EH: Ratio scales and category scales for a dozen perceptual continua. Journal of Experimental Psychology 1957,54(6):377-411.

    Google Scholar 

  105. 105.

    Stevens SS: Concerning the psychophysical power law. Quarterly Journal of Experimental Psychology 1964,16(4):383-385. 10.1080/17470216408416398

    Google Scholar 

  106. 106.

    Naka KI, Rushton WA: S-potentials from luminosity units in the retina of fish ( Cyprinidae ). Journal of Physiology 1966,185(3):587-599.

    Google Scholar 

  107. 107.

    Normann RA, Werblin FS: Control of retinal sensitivity. I. Light and dark adaptation of vertebrate rods and cones. Journal of General Physiology 1974,63(1):37-61. 10.1085/jgp.63.1.37

    Google Scholar 

  108. 108.

    Hood DC, Finkelstein MA, Buckingham E: Psychophysical tests of models of the response function. Vision Research 1979,19(4):401-406. 10.1016/0042-6989(79)90104-4

    Google Scholar 

  109. 109.

    Ekman G: Is the power law a special case of Fechner's law? Perceptual and Motor Skills 1964, 19: 730. 10.2466/pms.1964.19.3.730

    Google Scholar 

  110. 110.

    Kvalseth TO: Is Fechner's logarithmic law a special case of Stevens' power law? Perceptual and Motor Skills 1981, 52: 617–618. 10.2466/pms.1981.52.2.617

    Google Scholar 

  111. 111.

    McGill WJ, Goldberg JP: A study of the near-miss involving Weber's law and pure intensity discrimination. Perceptual Psychophysics 1968, 4: 105–109. 10.3758/BF03209518

    Google Scholar 

  112. 112.

    Graf V, Baird JC, Glesman G: An empirical test of two psychophysical models. Acta Psychologica 1974,38(1):59-72. 10.1016/0001-6918(74)90029-8

    Google Scholar 

  113. 113.

    Heeger DJ: Normalization of cell responses in cat striate cortex. Visual Neuroscience 1992,9(2):181-197. 10.1017/S0952523800009640

    MathSciNet  Google Scholar 

  114. 114.

    Schwartz O, Simoncelli EP: Natural signal statistics and sensory gain control. Nature Neuroscience 2001,4(8):819-825. 10.1038/90526

    Google Scholar 

  115. 115.

    Ledda P, Santos LP, Chalmers A: A local model of eye adaptation for high dynamic range images. Proceedings of the 3rd International Conference on Computer Graphics, Virtual Reality, Visualization and Interaction in Africa, November 2004, Stellenbosch, South Africa 151–160.

    Google Scholar 

  116. 116.

    Malo J, Epifanio I, Navarro R, Simoncelli EP: Nonlinear image representation for efficient perceptual coding. IEEE Transactions on Image Processing 2006,15(1):68-80.

    Google Scholar 

  117. 117.

    Watson AB: DCT quantization matrices visually optimized for individual images. Human Vision, Visual Processing, and Digital Display IV, February 1993, San Jose, Calif, USA, Proceedings of SPIE 1913: 202–216.

    Google Scholar 

  118. 118.

    Simoncelli EP, Olshausen BA: Natural image statistics and neural representation. Annual Review of Neuroscience 2001, 24: 1193–1216. 10.1146/annurev.neuro.24.1.1193

    Google Scholar 

  119. 119.

    Hyvarinen A, Karhunen J, Oja E: Independent Component Analysis. John Wiley & Sons, New York, NY, USA; 2001.

    Google Scholar 

  120. 120.

    Watson AB: Efficiency of a model human image code. Journal of the Optical Society of America A 1987,4(12):2401-2417. 10.1364/JOSAA.4.002401

    Google Scholar 

  121. 121.

    Olshausen BA, Field DJ: Emergence of simple-cell receptive field properties by learning a sparse code for natural images. Nature 1996,381(6583):607-609. 10.1038/381607a0

    Google Scholar 

  122. 122.

    Malo J, Pons AM, Felipe A, Artigas JM: Characterization of the human visual system threshold performance by a weighting function in the Gabor domain. Journal of Modern Optics 1997,44(1):127-148.

    Google Scholar 

  123. 123.

    Field DJ: Relations between the statistics of natural images and the response properties of cortical cells. Journal of the Optical Society of America A 1987,4(12):2379-2394. 10.1364/JOSAA.4.002379

    Google Scholar 

  124. 124.

    Watson AB, Solomon JA: Model of visual contrast gain control and pattern masking. Journal of the Optical Society of America A 1997,14(9):2379-2391. 10.1364/JOSAA.14.002379

    Google Scholar 

  125. 125.

    Paranjape RB, Rangayyan RM, Morrow WM: Adaptive neighbourhood mean and median image filtering. Journal of Electronic Imaging 1994,3(4):360-367. 10.1117/12.180118

    Google Scholar 

  126. 126.

    Rangayyan RM, Ciuc M, Faghih F: Adaptive-neighborhood filtering of images corrupted by signal-dependent noise. Applied Optics 1998,37(20):4477-4487. 10.1364/AO.37.004477

    Google Scholar 

  127. 127.

    Munkres JR: Topology. 2nd edition. Prentice-Hall, Englewood Cliffs, NJ, USA; 2000.

    Google Scholar 

  128. 128.

    Jalobeanu A, Blanc-Féraud L, Zerubia J: An adaptive Gaussian model for satellite image deblurring. IEEE Transactions on Image Processing 2004,13(4):613-621. 10.1109/TIP.2003.819969

    Google Scholar 

  129. 129.

    Goutsias J, Heijmans HJAM: Nonlinear multiresolution signal decomposition schemes. Part I: morphological pyramids. IEEE Transactions on Image Processing 2000,9(11):1862-1876. 10.1109/83.877209

    MathSciNet  MATH  Google Scholar 

  130. 130.

    Mallat SG: A theory for multiresolution signal decomposition: the wavelet representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 1989,11(7):674-693. 10.1109/34.192463

    MATH  Google Scholar 

  131. 131.

    Lindeberg T: Scale-space theory: a basic tool for analysing structures at different scales. Journal of Applied Statistics 1994,21(2):225-270.

    Google Scholar 

  132. 132.

    Perona P, Malik J: Scale-space and edge detection using anisotropic diffusion. IEEE Transactions on Pattern Analysis and Machine Intelligence 1990,12(7):629-639. 10.1109/34.56205

    Google Scholar 

  133. 133.

    Kanisza G: Organization in Vision. Holt, Rinehart and Winston, New York, NY, USA; 1979.

    Google Scholar 

  134. 134.

    Palmer SE, Brooks JL, Nelson R: When does grouping happen? Acta Psychologica 2003,114(3):311-330. 10.1016/j.actpsy.2003.06.003

    Google Scholar 

  135. 135.

    Hess R, Field D: Integration of contours: new insights. Trends in Cognitive Sciences 1999,3(12):480-486. 10.1016/S1364-6613(99)01410-2

    Google Scholar 

  136. 136.

    Dakin SC, Herbert AM: The spatial region of integration for visual symmetry detection. Proceedings of the Royal Society B: Biological Sciences 1998,265(1397):659-664. 10.1098/rspb.1998.0344

    Google Scholar 

  137. 137.

    Dakin SC, Hess RF: The spatial mechanisms mediating symmetry perception. Vision Research 1997,37(20):2915-2930. 10.1016/S0042-6989(97)00031-X

    Google Scholar 

  138. 138.

    Dakin SC, Watt RJ: Detection of bilateral symmetry using spatial filters. Spatial Vision 1994,8(4):393-413. 10.1163/156856894X00071

    Google Scholar 

  139. 139.

    Di Gesù V, Valenti C: Symmetry operators in computer vision. Vistas in Astronomy 1996,40(4):461-468. 10.1016/S0083-6656(96)00030-X

    Google Scholar 

  140. 140.

    Rom H, Medioni G: Hierarchical decomposition and axial shape description. IEEE Transactions on Pattern Analysis and Machine Intelligence 1993,15(10):973-981. 10.1109/34.254054

    Google Scholar 

  141. 141.

    Kimia BB: On the role of medial geometry in human vision. Journal of Physiology-Paris 2003,97(2-3):155-190. 10.1016/j.jphysparis.2003.09.003

    Google Scholar 

  142. 142.

    Gonzalez RC, Woods RE: Digital Image Processing. Addison-Wesley, Reading, Mass, USA; 1992.

    Google Scholar 

  143. 143.

    Matheron G: Eléments pour une Théorie des Milieux Poreux. Masson, Paris, France; 1967.

    Google Scholar 

  144. 144.

    Soille P: Morphological Image Analysis. Principles and Applications. Springer, New York, NY, USA; 2003.

    Google Scholar 

  145. 145.

    Serra J: Image Analysis and Mathematical Morphology: Vol. 2: Theoretical Advances. Academic Press, London, UK; 1988.

    Google Scholar 

  146. 146.

    Charif-Chefchaouni M, Schonfeld D: Spatially-variant mathematical morphology. Proceedings of IEEE International Conference Image Processing (ICIP '94), November 1994, Austin, Tex, USA 2: 555–559.

    MATH  Google Scholar 

  147. 147.

    Lerallut R, Decenciére E, Meyer F: Image filtering using morphological amoebas. In Proceedings of the 7th International Symposium on Mathematical Morphology (ISMM '05), April 2005, Paris, France Edited by: Ronse C, Najman L, Decenciére E. 13–22.

    Google Scholar 

  148. 148.

    Cuisenaire O: Locally adaptable mathematical morphology. Proceedings of IEEE International Conference on Image Processing (ICIP '05), September 2005, Genova, Italy 2: 125–128.

    MATH  Google Scholar 

  149. 149.

    Cech E: Topological Spaces. John Wiley & Sons, Prague, Czechoslovakia; 1966.

    Google Scholar 

  150. 150.

    Serra J, Salembier P: Connected operators and pyramids. Image Algebra and Morphological Image Processing IV, July 1993, San Diego, Calif, USA, Proceedings of SPIE 2030: 65–76.

    MathSciNet  Google Scholar 

  151. 151.

    Matheron G: Filters and lattices. In Image Analysis and Mathematical Morphology. Volume 2 : Theoretical Advances. Academic Press, London, UK; 1988:115-140.

    Google Scholar 

  152. 152.

    Beucher S, Lantuejoul C: Use of watersheds in contour detection. Proceedings of International Workshop on Image Processing, Real-Time Edge and Motion Detection/Estimation, September 1979, Rennes, France 17–21.

    Google Scholar 

  153. 153.

    Gain P, Thuret G, Kodjikian L, et al.: Automated tri-image analysis of stored corneal endothelium. British Journal of Ophthalmology 2002,86(7):801-808. 10.1136/bjo.86.7.801

    Google Scholar 

  154. 154.

    Chazallon L, Pinoli J-C: An automatic morphological method for aluminium grain segmentation in complex grey level images. Acta Stereologica 1997,16(2):119-130.

    Google Scholar 

  155. 155.

    Ramponi G, Strobel N, Mitra SK, Yu T-H: Nonlinear unsharp masking methods for image contrast enhancement. Journal of Electronic Imaging 1996,5(3):353-366. 10.1117/12.242618

    Google Scholar 

Download references

Author information

Affiliations

Authors

Corresponding author

Correspondence to J-C Pinoli.

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Pinoli, J., Debayle, J. Logarithmic Adaptive Neighborhood Image Processing (LANIP): Introduction, Connections to Human Brightness Perception, and Application Issues. EURASIP J. Adv. Signal Process. 2007, 036105 (2006). https://doi.org/10.1155/2007/36105

Download citation

Keywords

  • Image Segmentation
  • Image Enhancement
  • Image Representation
  • Image Restoration
  • Imaging Application