高动态图像视觉保真实时显示变换技术
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摘要
尽管对图像生成技术的研究给人们提供了越来越好的结果和越来越快速的方法,目前的图像产生过程仍然不足以确保感觉上的逼真性。由于常规图像显示设备只适合输出小动态范围亮度图像,使人们不能观察到场景图像的全部信息,而这一过程又恰恰是整个图像产生过程的重要部分。特别是近年来高动态范围图像(high dynamic range image-HDRI)在计算机图形学领域变得越来越普遍而且重要,真实场景的HDR 图像也变得非常容易获取,图像显示硬件的局限性也就越发暴露出来。
本文通过在亮度图像梯度域上对大梯度进行衰减,压缩图像亮度的动态范围,可以使高动态范围图像在被显示时,既能够适应常规的显示硬件,同时又充分保留了原始图像的细节信息,使得图像在被观察时能够重现真实场景的亮度效果。通过对小梯度的适当提升,使得低动态范围图像得以增强,从而更易于观察图像的细微细节。
当前,GPU的图形管线为我们提供了良好的可编程性支持,计算机图形硬件架构已经越来越强调其通用性。主流PC 机上的GPU 除了可以作为向量以及流式信息处理器,它完全适用于更加强大的通用数值计算。
本文采用适合由图形处理器加速的快速算法,借助可编程图形硬件和先进的图形应用程序加速接口,将动态范围变换的整个处理过程通过可编程图形硬件实现,建立快速的图像动态范围压缩技术,建立起适用于高动态范围图像显示的实时应用框架,使之不仅适用于基于图像的动态范围调整的绝大部分情况,应用于数字摄影、电影艺术、科学图像增强,还能够成为目前交互式图形应用的核心技术,成为实现虚拟实景、交互3D 应用的基础之一。
While research into ways of creating images provides us with better and faster methods, we may not see the full effect of these techniques due to display limitations. However, displaying an image is also an important part of the overall process, and weaknesses in this area may significantly detract from advances made in image creation. Today, high dynamic range (HDR) radiance maps are becoming increasingly common and important in computer graphics and HDR maps of real scenes are very easy to construct, while the limitations of display devices are more and more exposed.
We present a system for compressing high dynamic range images to fit conventional display devices that are only capable of outputting a low dynamic range. In addition to manipulating the gradient field of luminance image by attenuating the large gradients‘magnitudes, it can preserve fine details, resulting in an image which provokes the same responses as someone would have viewing the scene in the real world.
Recently, graphics hardware architectures have begun to emphasize versatility, offering rich new ways to programmatically reconfigure the graphics pipeline. As a result, powerful and potentially general-purpose constructs not unlike vector and stream processors are appearing in commodity PC machines, thanks to their graphics chips.
Specifically, with the whole process built on programmable graphics hardware, we present an efficient algorithm based on GPU acceleration and provide a fast dynamic range compression technique. Furthermore, we describe a framework for rendering high dynamic range images in real time. It not only can be used in image based tone mapping but also is able to be a core technique in interactive graphics applications.
本文通过在亮度图像梯度域上对大梯度进行衰减,压缩图像亮度的动态范围,可以使高动态范围图像在被显示时,既能够适应常规的显示硬件,同时又充分保留了原始图像的细节信息,使得图像在被观察时能够重现真实场景的亮度效果。通过对小梯度的适当提升,使得低动态范围图像得以增强,从而更易于观察图像的细微细节。
当前,GPU的图形管线为我们提供了良好的可编程性支持,计算机图形硬件架构已经越来越强调其通用性。主流PC 机上的GPU 除了可以作为向量以及流式信息处理器,它完全适用于更加强大的通用数值计算。
本文采用适合由图形处理器加速的快速算法,借助可编程图形硬件和先进的图形应用程序加速接口,将动态范围变换的整个处理过程通过可编程图形硬件实现,建立快速的图像动态范围压缩技术,建立起适用于高动态范围图像显示的实时应用框架,使之不仅适用于基于图像的动态范围调整的绝大部分情况,应用于数字摄影、电影艺术、科学图像增强,还能够成为目前交互式图形应用的核心技术,成为实现虚拟实景、交互3D 应用的基础之一。
While research into ways of creating images provides us with better and faster methods, we may not see the full effect of these techniques due to display limitations. However, displaying an image is also an important part of the overall process, and weaknesses in this area may significantly detract from advances made in image creation. Today, high dynamic range (HDR) radiance maps are becoming increasingly common and important in computer graphics and HDR maps of real scenes are very easy to construct, while the limitations of display devices are more and more exposed.
We present a system for compressing high dynamic range images to fit conventional display devices that are only capable of outputting a low dynamic range. In addition to manipulating the gradient field of luminance image by attenuating the large gradients‘magnitudes, it can preserve fine details, resulting in an image which provokes the same responses as someone would have viewing the scene in the real world.
Recently, graphics hardware architectures have begun to emphasize versatility, offering rich new ways to programmatically reconfigure the graphics pipeline. As a result, powerful and potentially general-purpose constructs not unlike vector and stream processors are appearing in commodity PC machines, thanks to their graphics chips.
Specifically, with the whole process built on programmable graphics hardware, we present an efficient algorithm based on GPU acceleration and provide a fast dynamic range compression technique. Furthermore, we describe a framework for rendering high dynamic range images in real time. It not only can be used in image based tone mapping but also is able to be a core technique in interactive graphics applications.
引文
[1] Debevec, Ward, and Lemmon, HDRI and Image-Based Lighting, SIGGRAPH 2003 Course #19
[2] James A. etc. A model of visual adaptation for realistic image synthesis. In Proceedings of SIGGRAPH 96, Computer Graphics Proceedings, Annual Conference Series, pages 249–258, New Orleans, Louisiana, August 1996. ACM SIGGRAPH / AddisonWesley. ISBN 0-201-94800-1.
[3] Sumanta N. Pattanaik. etc. A multiscale model of adaptation and spatial vision for realistic image display. In Proceedings of SIGGRAPH 98, Computer Graphics Proceedings, Annual Conference Series, pages 287–298, Orlando, Florida, July 1998. ACM SIGGRAPH / AddisonWesley. ISBN 0-89791-999-8.
[4] Sumanta N. Pattanaik. etc. Time-dependent visual adaptation for realistic image display. In Proceedings of ACM SIGGRAPH 2000, Computer Graphics Proceedings, Annual Conference Series, pages 47–54. ACM Press / ACM SIGGRAPH / Addison Wesley Longman, July 2000. ISBN 1-58113-208-5.
[5] J. Tumblin. Three Methods of Detail-Preserving Contrast Reduction for Displayed Images. Phd thesis, Georgia Institute of Technology, December 1999.
[6] 何烽,徐之海等,一种基于数字图像合成的扩展动态范围方法,光电工程,2003 年20月第30 卷第5 期66-68 页
[7] AGGARWAL, M., AND AHUJA, N. 2001. High dynamic range panoramic imaging. In Proc. IEEE ICCV, vol. I, 2–9.
[8] NAYAR, S. K., AND MITSUNAGA, T. 2000. High dynamic range imaging: Spatially varying pixel exposures. In Proc. IEEE CVPR.
[9] N.J. Miller, P.Y. Ngai, and D.D. Miller. The application of computer graphics in lighting design. Journal of the IES, 14:6–26, 1984.
[10] S.D. Upstill. The Realistic Presentation of Synthetic Images. PhD thesis, Computer Science Division, University of California, Berkeley, 1985.
[11] Jack Tumblin and Holly E. Rushmeier. Tone reproduction for realistic images. IEEE Computer Graphics & Applications, 13(6):42–48, November 1993.
[12] Greg Ward. A contrast-based scalefactor for luminance display. In Graphics Gems IV, pages 415–421. Academic Press, Boston, 1994. ISBN 0-12-336155-9.
[13] Gregory Ward Larson, ect. A visibility matching tone reproduction operator for high dynamic range scenes. IEEE Transactions on Visualization and Computer Graphics, 3(4):291–306, October –December 1997. ISSN 1077-2626.
[14] A. Scheel, etc. Tone reproduction for interactive walkthroughs. Computer Graphics Forum, 19(3):301–312, August 2000. ISSN 1067-7055.
[15] A. Oppenheim, etc. Nonlinear filtering of multiplied and convolved signals. In Proceedings of the IEEE, volume 56, pages 1264–1291, August 1968.
[16] K. Chiu, M. Herf, P. Shirley, S. Swamy, C. Wang, and K. Zimmerman. Spatially nonuniform scaling functions for high contrast images. In Graphics Interface ’93, pages 245–253, Toronto, Ontario, Canada, May 1993. Canadian Information Processing Society.
[17] D. J. Jobson, etc. A multiscale retinex for bridging the gap between color images and the human observation of scenes. IEEE Transactions on Image Processing, 6(7):965–976, July 1997.
[18] Jack Tumblin and Greg Turk. Lcis: A boundary hierarchy for detail-preserving contrast reduction. In Proceedings of SIGGRAPH 99, Computer Graphics Proceedings, Annual Conference Series, pages 83–90, Los Angeles, California, August 1999. ACM SIGGRAPH / Addison Wesley Longman. ISBN 0-20148-560-5.
[19] Fr′edo Durand and Julie Dorsey. Fast bilateral filtering for the display of high dynamic range image. In John Hughes, editor, SIGGRAPH 2002 Conference Graphics Proceedings, Annual Conference Series, pages 257–265. ACM Press/ACM SIGGRAPH, 2002.
[20] Erik Reinhard, Michael Stark, Peter Shirley, and Jim Ferwerda. Photographic tone reproduction for digital images. In Proceedings of ACM SIGGRAPH 2002, Computer Graphics Proceedings, Annual Conference Series. ACM Press / ACM SIGGRAPH, July 2002.
[21] Raanan Fattal, Dani Lischinski, and Micheal Werman. Gradient domain high dynamic range compression. In Proceedings of ACM SIGGRAPH 2002, Computer Graphics Proceedings, Annual Conference Series. ACM Press / ACM SIGGRAPH, July 2002.
[22] R.W. G. Hunt. The Reproduction of Colour in Photography, Printing and Television. Fountain Press, Tolworth, 5th edition, 1995.
[23] W. H., TEUKOLSKY, etc. 1992. Numerical Recipes in C: The Art of Scientific Computing, 2nd ed. Cambridge University Press.
[24] Nolan Goodnight1, etc. A Multigrid Solver for Boundary Value Problems Using Programmable Graphics Hardware. Graphics Hardware 2003, pp. 1–11
[25] NVIDIA. GeForceFX, 2003. http://www.nvidia.com/view.asp?PAGE=fx_desktop.
[26] ATI. RADEON? 9700, 2003. http://www.ati.com/products/gamer.html
[27] Kekoa Proudfoot, etc. A real time procedural shading system for programmable graphics hardware. In Proceedings of SIGGRAPH 2001, pages 159–170, August 2001.
[28] Kris Gray. Microsoft Directx9 Programmable Graphics Pipeline. Microsoft Press 2003
[29] Tim Purcell, ect. Ray tracing on programmable graphics hardware. ACM Transactions on Graphics, 21(3):703–712, July 2002.
[30] KHAILANY, B. etc. 2001. Imagine: Media Processing with Streams. IEEE Micro 21, 2, 35–46.
[31] OWENS, J. D. etc. 2002. Comparing Reyes and OpenGL on a Stream Architecture. In SIGGRAPH/ Eurographics Workshop on Graphics Hardware, 47–56.
[32] HARRIS, M. J. etc. 2002. Physically-Based Visual Simulation on Graphics Hardware. In SIGGRAPH/Eurographics Workshop on Graphics Hardware.
[33] LI, W.etc. 2003. Implementing Lattice Boltzmann Comptuation on Graphics Hardware. The Visual Computer. To appear.
[34] STRZODKA, R., AND RUMPF, M. 2001. Nonlinear Diffusion in Graphics Hardware. In Visualization, 75–84.
[35] http://www.nvidia.com/object/gpu.html
[36] http://www.gpgpu.org
[37] Jens Krüger and Rüdiger Westermann. Linear Algebra Operators for GPU
[2] James A. etc. A model of visual adaptation for realistic image synthesis. In Proceedings of SIGGRAPH 96, Computer Graphics Proceedings, Annual Conference Series, pages 249–258, New Orleans, Louisiana, August 1996. ACM SIGGRAPH / AddisonWesley. ISBN 0-201-94800-1.
[3] Sumanta N. Pattanaik. etc. A multiscale model of adaptation and spatial vision for realistic image display. In Proceedings of SIGGRAPH 98, Computer Graphics Proceedings, Annual Conference Series, pages 287–298, Orlando, Florida, July 1998. ACM SIGGRAPH / AddisonWesley. ISBN 0-89791-999-8.
[4] Sumanta N. Pattanaik. etc. Time-dependent visual adaptation for realistic image display. In Proceedings of ACM SIGGRAPH 2000, Computer Graphics Proceedings, Annual Conference Series, pages 47–54. ACM Press / ACM SIGGRAPH / Addison Wesley Longman, July 2000. ISBN 1-58113-208-5.
[5] J. Tumblin. Three Methods of Detail-Preserving Contrast Reduction for Displayed Images. Phd thesis, Georgia Institute of Technology, December 1999.
[6] 何烽,徐之海等,一种基于数字图像合成的扩展动态范围方法,光电工程,2003 年20月第30 卷第5 期66-68 页
[7] AGGARWAL, M., AND AHUJA, N. 2001. High dynamic range panoramic imaging. In Proc. IEEE ICCV, vol. I, 2–9.
[8] NAYAR, S. K., AND MITSUNAGA, T. 2000. High dynamic range imaging: Spatially varying pixel exposures. In Proc. IEEE CVPR.
[9] N.J. Miller, P.Y. Ngai, and D.D. Miller. The application of computer graphics in lighting design. Journal of the IES, 14:6–26, 1984.
[10] S.D. Upstill. The Realistic Presentation of Synthetic Images. PhD thesis, Computer Science Division, University of California, Berkeley, 1985.
[11] Jack Tumblin and Holly E. Rushmeier. Tone reproduction for realistic images. IEEE Computer Graphics & Applications, 13(6):42–48, November 1993.
[12] Greg Ward. A contrast-based scalefactor for luminance display. In Graphics Gems IV, pages 415–421. Academic Press, Boston, 1994. ISBN 0-12-336155-9.
[13] Gregory Ward Larson, ect. A visibility matching tone reproduction operator for high dynamic range scenes. IEEE Transactions on Visualization and Computer Graphics, 3(4):291–306, October –December 1997. ISSN 1077-2626.
[14] A. Scheel, etc. Tone reproduction for interactive walkthroughs. Computer Graphics Forum, 19(3):301–312, August 2000. ISSN 1067-7055.
[15] A. Oppenheim, etc. Nonlinear filtering of multiplied and convolved signals. In Proceedings of the IEEE, volume 56, pages 1264–1291, August 1968.
[16] K. Chiu, M. Herf, P. Shirley, S. Swamy, C. Wang, and K. Zimmerman. Spatially nonuniform scaling functions for high contrast images. In Graphics Interface ’93, pages 245–253, Toronto, Ontario, Canada, May 1993. Canadian Information Processing Society.
[17] D. J. Jobson, etc. A multiscale retinex for bridging the gap between color images and the human observation of scenes. IEEE Transactions on Image Processing, 6(7):965–976, July 1997.
[18] Jack Tumblin and Greg Turk. Lcis: A boundary hierarchy for detail-preserving contrast reduction. In Proceedings of SIGGRAPH 99, Computer Graphics Proceedings, Annual Conference Series, pages 83–90, Los Angeles, California, August 1999. ACM SIGGRAPH / Addison Wesley Longman. ISBN 0-20148-560-5.
[19] Fr′edo Durand and Julie Dorsey. Fast bilateral filtering for the display of high dynamic range image. In John Hughes, editor, SIGGRAPH 2002 Conference Graphics Proceedings, Annual Conference Series, pages 257–265. ACM Press/ACM SIGGRAPH, 2002.
[20] Erik Reinhard, Michael Stark, Peter Shirley, and Jim Ferwerda. Photographic tone reproduction for digital images. In Proceedings of ACM SIGGRAPH 2002, Computer Graphics Proceedings, Annual Conference Series. ACM Press / ACM SIGGRAPH, July 2002.
[21] Raanan Fattal, Dani Lischinski, and Micheal Werman. Gradient domain high dynamic range compression. In Proceedings of ACM SIGGRAPH 2002, Computer Graphics Proceedings, Annual Conference Series. ACM Press / ACM SIGGRAPH, July 2002.
[22] R.W. G. Hunt. The Reproduction of Colour in Photography, Printing and Television. Fountain Press, Tolworth, 5th edition, 1995.
[23] W. H., TEUKOLSKY, etc. 1992. Numerical Recipes in C: The Art of Scientific Computing, 2nd ed. Cambridge University Press.
[24] Nolan Goodnight1, etc. A Multigrid Solver for Boundary Value Problems Using Programmable Graphics Hardware. Graphics Hardware 2003, pp. 1–11
[25] NVIDIA. GeForceFX, 2003. http://www.nvidia.com/view.asp?PAGE=fx_desktop.
[26] ATI. RADEON? 9700, 2003. http://www.ati.com/products/gamer.html
[27] Kekoa Proudfoot, etc. A real time procedural shading system for programmable graphics hardware. In Proceedings of SIGGRAPH 2001, pages 159–170, August 2001.
[28] Kris Gray. Microsoft Directx9 Programmable Graphics Pipeline. Microsoft Press 2003
[29] Tim Purcell, ect. Ray tracing on programmable graphics hardware. ACM Transactions on Graphics, 21(3):703–712, July 2002.
[30] KHAILANY, B. etc. 2001. Imagine: Media Processing with Streams. IEEE Micro 21, 2, 35–46.
[31] OWENS, J. D. etc. 2002. Comparing Reyes and OpenGL on a Stream Architecture. In SIGGRAPH/ Eurographics Workshop on Graphics Hardware, 47–56.
[32] HARRIS, M. J. etc. 2002. Physically-Based Visual Simulation on Graphics Hardware. In SIGGRAPH/Eurographics Workshop on Graphics Hardware.
[33] LI, W.etc. 2003. Implementing Lattice Boltzmann Comptuation on Graphics Hardware. The Visual Computer. To appear.
[34] STRZODKA, R., AND RUMPF, M. 2001. Nonlinear Diffusion in Graphics Hardware. In Visualization, 75–84.
[35] http://www.nvidia.com/object/gpu.html
[36] http://www.gpgpu.org
[37] Jens Krüger and Rüdiger Westermann. Linear Algebra Operators for GPU