PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
2015 | 25 | 4 | 943-954
Tytuł artykułu

High dynamic range imaging by perceptual logarithmic exposure merging

Treść / Zawartość
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
In this paper we emphasize a similarity between the logarithmic type image processing (LTIP) model and the Naka-Rushton model of the human visual system (HVS). LTIP is a derivation of logarithmic image processing (LIP), which further replaces the logarithmic function with a ratio of polynomial functions. Based on this similarity, we show that it is possible to present a unifying framework for the high dynamic range (HDR) imaging problem, namely, that performing exposure merging under the LTIP model is equivalent to standard irradiance map fusion. The resulting HDR algorithm is shown to provide high quality in both subjective and objective evaluations.
Rocznik
Tom
25
Numer
4
Strony
943-954
Opis fizyczny
Daty
wydano
2015
otrzymano
2014-11-03
poprawiono
2015-03-24
Twórcy
  • Image Processing and Analysis Laboratory, University "Politehnica" of Bucharest, Splaiul Independenţei 313, Bucharest, Romania
  • Image Processing and Analysis Laboratory, University "Politehnica" of Bucharest, Splaiul Independenţei 313, Bucharest, Romania
autor
  • Image Processing and Analysis Laboratory, University "Politehnica" of Bucharest, Splaiul Independenţei 313, Bucharest, Romania
Bibliografia
  • Aydin, T., Mantiuk, R., Myszkowski, K. and Seidel, H. (2008). Dynamic range independent image quality assessment, ACM Transactions on Graphics 27(3): 1-10.
  • Banterle, F., Artusi, A., Debattista, K. and Chalmers, A. (2011). Advanced High Dynamic Range Imaging: Theory and Practice, AK Peters (CRC Press), Natick, MA.
  • Banterle, F., Artusi, A., Sikudova, E., Edward, T., Bashford-Rogers, W., Ledda, P., Bloj, M. and Chalmers, A. (2012). Dynamic range compression by differential zone mapping based on psychophysical experiments, ACM Symposium on Applied Perception, Los Angeles, CA, USA, pp. 39-46.
  • Barten, P.G.J. (1999). Contrast Sensitivity of the Human Eye and Its Effects on Image Quality, SPIE, Washington, DC.
  • Bruce, N.D. (2014). Expoblend: Information preserving exposure blending based on normalized log-domain entropy, Computers & Graphics 39: 12-23.
  • Čadík, M., Wimmer, M., Neumann, L. and Artusi, A. (2008). Evaluation of HDR tone mapping methods using essential perceptual attributes, Computers & Graphics 32(3): 330-349.
  • Debevec, P. and Malik, J. (1997). Recovering high dynamic range radiance maps from photographs, ACM SIGGRAPH, pp. 369-378.
  • Deng, G., Cahill, L.W. and Tobin, G.R. (1995). A study of logarithmic image processing model and its application to image enhancement, IEEE Transactions on Image Processing 4(4): 506-512.
  • Drago, F., Myszkowski, K., Annen, T. and Chiba, N. (2003). Adaptive logarithmic mapping for displaying high contrast scenes, Computer Graphics Forum 22(3): 419-426.
  • Durand, F. and Dorsey, J. (2002). Fast bilateral filtering for the display of high-dynamic-range images, ACM Transactions on Graphics 21(3): 257-266.
  • Fattal, R., Lischinski, D. and Werman, M. (2002). Gradient domain high dynamic range compression, ACM Transactions on Graphics 21(3): 249-256.
  • Ferradans, S., Bertalmio, M., Provenzi, E. and Caselles, V. (2012). An analysis of visual adaptation and contrast perception for tone mapping, IEEE Transactions on Pattern Analysis and Machine Intelligence 33(10): 2002-2012.
  • Florea, C. and Florea, L. (2013). Parametric logarithmic type image processing for contrast based auto-focus in extreme lighting conditions, International Journal of Applied Mathematics and Computer Science 23(3): 637-648, DOI: 10.2478/amcs-2013-0048.
  • Gilchrist, A., Kossyfidis, C., Bonato, F., Agostini, T., Cataliotti, J., Li, X., Spehar, B., Annan, V. and Economou, E. (1999). An anchoring theory of lightness perception, Psychological Review 106(4): 795-834.
  • Grossberg, M.D. and Nayar, S.K. (2004). Modeling the space of camera response functions, IEEE Transactions on Pattern Analysis and Machine Intelligence 26(10): 1272-1282.
  • Jourlin, M. and Pinoli, J.C. (1987). Logarithmic image processing, Acta Stereologica 6: 651-656.
  • Krawczyk, G., Myszkowski, K. and Seidel, H.-P. (2005). Lightness perception in tone reproduction for high dynamic range images, Computer Graphics Forum 24(3): 635-645.
  • Macmillan, N. and Creelman, C. (Eds.) (2005). Detection Theory: A User's Guide, Lawrence Erlbaum, London.
  • Mann, S. and Mann, R. (2001). Quantigraphic imaging: Estimating the camera response and exposures from differently exposed images, IEEE Computer Vision and Pattern Recognition, Kauai, HI, USA, Vol. 1, pp. 842-849.
  • Mann, S. and Picard, R. (1995). Being ‘undigital' with digital cameras: Extending dynamic range by combining differently exposed pictures, Proceedings of IS&Ts 48th Annual Conference, San Jose, CA, USA, Vol. 1, pp. 422-428.
  • Marković, D. and Jukić, D. (2013). On parameter estimation in the bass model by nonlinear least squares fitting the adoption curve, International Journal of Applied Mathematics and Computer Science 23(1): 145-155, DOI: 10.2478/amcs-2013-0012.
  • Mertens, T., Kautz, J. and Reeth, F.V. (2007). Exposure fusion, Proceedings of Pacific Graphics, Maui, HI, USA, pp. 382-390.
  • Meylan, L., Alleysson, D. and Susstrunk, S. (2007). Model of retinal local adaptation for the tone mapping of color filter array images, Journal of Optical Society of America A 24(9): 2807-2816.
  • Naka, K.-I. and Rushton, W.A.H. (1966). S-potentials from luminosity units in the retina of fish (cyprinidae), The Journal of Physiology 185(3): 587-599.
  • Navarro, L., Courbebaisse, G. and Deng, G. (2013). The symmetric logarithmic image processing model, Digital Signal Processing 23(5): 1337-1343.
  • Panetta, K., Zhou, Y., Agaian, S. and Wharton, E. (2011). Parameterized logarithmic framework for image enhancement, IEEE Transactions on Systems, Man, and Cybernetics B: Cybernetics 41(2): 460-472.
  • Patrascu, V. and Buzuloiu, V. (2001). Color image enhancement in the framework of logarithmic models, 8th IEEE International Conference on Telecommunications, Bucharest, Romania, Vol. 1, pp. 199-204.
  • Pece, F. and Kautz, J. (2010). Bitmap movement detection: HDR for dynamic scenes, Proceedings of the Conference on Visual Media Production, London, UK, pp. 1-8.
  • Pinoli, J.C. and Debayle, J. (2007). Logarithmic adaptive neighborhood image processing (LANIP): Introduction, connections to human brightness perception, and application issues, EURASIP Journal on Advances in Signal Processing 1: 114-114, Paper no. 036105.
  • Reinhard, E., Stark, M., Shirley, P. and Ferwerda, J. (2002). Photographic tone reproduction for digital images, ACM Transactions on Graphics 21(3): 267-276.
  • Reinhard, E., Ward, G., Pattanaik, S. and Debevec, P. (2005). High Dynamic Range Imaging: Acquisition, Display and Image-Based Lighting, Morgan Kaufmann Publishers, San Francisco, CA.
  • Robertson, M., Borman, S. and Stevenson, R. (1999). Dynamic range improvement through multiple exposures, International Conference on Image Processing, Kobe, Japan, pp. 159-163.
  • Stevens, J. and Stevens, S. (1963). Brightness functions: Effects of adaptation, Journal of Optical Society of America A 53(3): 375-385.
  • Stevens, S. (1961). To honor Fechner and repeal his law, Science 133(3446): 80-133.
  • Tamburino, D., Alleysson, D., Meylan, L. and Strusstruk, S. (2008). Digital camera workflow for high dynamic range images using a model of retinal process, in D. Tamburrino, et al. (Eds.), IS&T/SPIE Electronic Imaging: Digital Photography IV, San Jose, CA, USA.
  • Valeton, J. and van Norren, D. (1983). Light adaptation of primate cones: An analysis based on extracellular data, Vision Research 23(12): 1539-1547.
  • Vertan, C., Oprea, A., Florea, C. and Florea, L. (2008). A pseudo-logarithmic framework for edge detection, Advanced Concepts for Intelligent Vision Systems, Juan-lesPins, France, pp. 637-644.
  • Wang, Z., Bovik, A.C., Sheikh, H.R. and Simoncelli, E.P. (2004). Image quality assessment: From error visibility to structural similarity, IEEE Transactions on Image Processing 13(4): 600-612.
  • Ward, G., Rushmeier, H. and Piatko, C. (1997). A visibility matching tone reproduction operator for high dynamic range scenes, IEEE Transactions on Visualization and Computer Graphics 3(4): 291-306.
  • Yeganeh, H. and Wang, Z. (2013). Objective quality assessment of tone mapped images, IEEE Transactions on Image Processing 22(2): 657-667.
  • Zhang, W. and Cham, W.-K. (2012). Gradient-directed multi-exposure composition, IEEE Transactios on Image Processing 21(4): 2318-2323.
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.bwnjournal-article-amcv25i4p943bwm
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.