Passive Tomography of Turbulence Strength
Abstract
Turbulence is studied extensively in remote sensing, astronomy, meteorology, aerodynamics and fluid dynamics. The strength of turbulence is a statistical measure of local variations in the turbulent medium. It influences engineering decisions made in these domains. Turbulence strength (TS) also affects safety of aircraft and tethered balloons, and reliability of free-space electromagnetic relays. We show that it is possible to estimate TS, without having to reconstruct instantaneous fluid flow fields. Instead, the TS field can be directly recovered, passively, using videos captured from different viewpoints. We formulate this as a linear tomography problem with a structure unique to turbulence fields. No tight synchronization between cameras is needed. Thus, realization is very simple to deploy using consumer-grade cameras. We experimentally demonstrate this both in a lab and in a large-scale uncontrolled complex outdoor environment, which includes industrial, rural and urban areas.
Keywords
Normalize Root Mean Square Error Phase Screen Background Oriented Schlieren Underwater Imaging Passive TomographyReferences
- 1.Aides, A., Schechner, Y.Y., Holodovsky, V., Garay, M.J., Davis, A.B.: Multi sky-view 3D aerosol distribution recovery. Optics Express 21(22), 25820–25833 (2013)CrossRefGoogle Scholar
- 2.Alterman, M., Schechner, Y.Y., Shamir, J., Perona, P.: Detecting motion through dynamic refraction. IEEE TPAMI 35, 245–251 (2013)CrossRefGoogle Scholar
- 3.Alterman, M., Schechner, Y.Y., Swirski, Y.: Triangulation in random refractive distortions. In: Proc. IEEE ICCP (2013)Google Scholar
- 4.Alterman, M., Swirski, Y., Schechner, Y.: STELLA MARIS: Stellar marine refractive imaging sensor. In: Proc. IEEE ICCP (2014)Google Scholar
- 5.Amanatides, J., Woo, A.: A fast voxel traversal algorithm for ray tracing. Eurographics 87(3), 3–10 (1987)Google Scholar
- 6.Atcheson, B., Heidrich, W., Ihrke, I.: An evaluation of optical flow algorithms for background oriented Schlieren imaging. Experiments in Fluids 46(3), 467–476 (2009)CrossRefGoogle Scholar
- 7.Atcheson, B., Ihrke, I., Heidrich, W., Tevs, A., Bradley, D., Magnor, M., Seidel, H.P.: Time-resolved 3D capture of non-stationary gas flows. ACM TOG 27(5), 132:1–132:9 (2008)Google Scholar
- 8.Couture, V., Martin, N., Roy, S.: Unstructured light scanning to overcome interreflections. In: Proc. IEEE ICCV, pp. 1895–1902 (2011)Google Scholar
- 9.Engelmann, R., Wandinger, U., Ansmann, A., Müller, D., Žeromskis, E., Althausen, D., Wehner, B.: Lidar observations of the vertical aerosol flux in the planetary boundary layer. J. Atmospheric & Oceanic Tech. 25(8), 1296–1306 (2008)CrossRefGoogle Scholar
- 10.Gilles, J., Dagobert, T., De Franchis, C.: Atmospheric turbulence restoration by diffeomorphic image registration and blind deconvolution. In: Blanc-Talon, J., Bourennane, S., Philips, W., Popescu, D., Scheunders, P. (eds.) ACIVS 2008. LNCS, vol. 5259, pp. 400–409. Springer, Heidelberg (2008)CrossRefGoogle Scholar
- 11.Gupta, M., Narasimhan, S.G., Schechner, Y.Y.: On controlling light transport in poor visibility environments. In: Proc. IEEE CVPR (2008)Google Scholar
- 12.Hansen, P.C., Saxild-Hansen, M.: AIR tools MATLAB package of algebraic iterative reconstruction methods. J. Computational and Applied Mathematics 236(8), 2167–2178 (2012)CrossRefzbMATHMathSciNetGoogle Scholar
- 13.Hargather, M.J., Settles, G.S.: Natural-background-oriented schlieren imaging. Experiments in Fluids 48(1), 59–68 (2010)CrossRefGoogle Scholar
- 14.Harmeling, S., Hirsch, M., Sra, S., Schölkopf, B.: Online blind deconvolution for astronomy. In: Proc. IEEE ICCP (2009)Google Scholar
- 15.Ihrke, I., Magnor, M.: Image-based tomographic reconstruction of flames. In: Proc. ACM/EG Sympos. on Animation, pp. 367–375 (2004)Google Scholar
- 16.Ihrke, I., Goidluecke, B., Magnor, M.: Reconstructing the geometry of flowing water. In: Proc. IEEE ICCV, vol. 2, pp. 1055–1060 (2005)Google Scholar
- 17.Ji, Y., Ye, J., Yu, J.: Reconstructing gas flows using light-path approximation. In: Proc. IEEE CVPR (2013)Google Scholar
- 18.Joshi, N., Cohen, M.F.: Seeing Mt. Rainier: Lucky imaging for multi-image denoising, sharpening, and haze removal. In: Proc. IEEE ICCP (2010)Google Scholar
- 19.Kaftory, R., Schechner, Y.Y., Zeevi, Y.Y.: Variational distance-dependent image restoration. In: Proc. IEEE CVPR (2007)Google Scholar
- 20.Kolmogorov, A.N.: Dissipation of energy in locally isotropic turbulence. Dokl. Akad. Nauk. SSSR 32, 16–18 (1941)zbMATHGoogle Scholar
- 21.Kolmogorov, A.N.: The local structure of turbulence in incompressible viscous fluid for very large reynolds numbers. Dokl. Akad. Nauk. SSSR 30, 299–303 (1941)Google Scholar
- 22.Kopeika, N.S.: A System Engineering Approach to Imaging. SPIE Press (1998)Google Scholar
- 23.Lalonde, J.F., Efros, A.A., Narasimhan, S.G.: Webcam clip art: Appearance and illuminant transfer from time-lapse sequences. ACM TOG 28(5), 131:1–131:10 (2009)Google Scholar
- 24.Lalonde, J.F., Narasimhan, S.G., Efros, A.A.: What do the sun and the sky tell us about the camera? IJCV 88(1), 24–51 (2010)CrossRefGoogle Scholar
- 25.Lucas, B., Kanade, T.: An iterative image registration technique with an application to stereo vision. IJCAI 81, 674–679 (1981)Google Scholar
- 26.Ma, C., Lin, X., Suo, J., Dai, Q., Wetzstein, G.: Transparent object reconstruction via coded transport of intensity. In: Proc. IEEE CVPR (2014)Google Scholar
- 27.Messer, H., Zinevich, A., Alpert, P.: Environmental sensor networks using existing wireless communication systems for rainfall and wind velocity measurements. IEEE Instrum. Meas. Mag., 32–38 (2012)Google Scholar
- 28.Morris, N., Kutulakos, K.: Dynamic refraction stereo. In: Proc. IEEE ICCV, vol. 2, pp. 1573–1580 (2005)Google Scholar
- 29.Narasimhan, S., Nayar, S.: Vision and the atmosphere. IJCV 48(3) (2002)Google Scholar
- 30.Narasimhan, S.G., Nayar, S.K., Sun, B., Koppal, S.J.: Structured light in scattering media. In: IEEE ICCV, vol. 1, pp. 420–427 (2005)Google Scholar
- 31.Oberlack, M., Peinke, J., Talamelli, A., Castillo, L., Hölling, M.: Progress in Turbulence Wind Energy IV. In: Proc. iTi Conf. in Turbulence (2010)Google Scholar
- 32.Ramlau, R., Rosensteiner, M.: An efficient solution to the atmospheric turbulence tomography problem using Kaczmarz iteration. Inverse Problems 28(9), 095004 (2012)Google Scholar
- 33.Richard, H., Raffel, M., Rein, M., Kompenhans, J., Meier, G.: Demonstration of the applicability of a background oriented Schlieren (BOS) method. In: Intl. Symp. on Applications of Laser Techniques to Fluid Mechanics, pp. 145–156 (2000)Google Scholar
- 34.Roggemann, M.C., Welsh, B.: Imaging Through Turbulence. CRC Press (1996)Google Scholar
- 35.Schechner, Y.Y.: A view through the waves. Marine Technology Society Journal 47, 148–150 (2013)CrossRefGoogle Scholar
- 36.Schechner, Y.Y., Diner, D.J., Martonchik, J.V.: Spaceborne underwater imaging. In: Proc. IEEE ICCP (2011)Google Scholar
- 37.Schechner, Y., Narasimhan, S., Nayar, S.: Polarization-based vision through haze. Applied Optics 42(3), 511–525 (2003)CrossRefGoogle Scholar
- 38.Sedlazeck, A., Koser, K., Koch, R.: 3D reconstruction based on underwater video from ROV Kiel 6000 considering underwater imaging conditions. In: Proc. IEEE OCEANS EUROPE (2009)Google Scholar
- 39.Settles, G.S.: Schlieren and Shadowgraph Techniques: Visualizing Phenomena in Transparent Media. Springer (2001)Google Scholar
- 40.Shimizu, M., Yoshimura, S., Tanaka, M., Okutomi, M.: Super-resolution from image sequence under influence of hot-air optical turbulence. In: Proc. IEEE CVPR (2008)Google Scholar
- 41.Swirski, Y., Schechner, Y.Y.: 3Deflicker from motion. In: Proc. IEEE ICCP (2013)Google Scholar
- 42.Tatarskii, V.: Wave Propagation in a Turbulent Medium. McGraw-Hill Books (1961)Google Scholar
- 43.Tian, Y., Narasimhan, S.G.: Seeing through water: Image restoration using model-based tracking. In: Proc. IEEE ICCV, pp. 2303–2310 (2009)Google Scholar
- 44.Tian, Y., Narasimhan, S., Vannevel, A.: Depth from optical turbulence. In: Proc. IEEE CVPR (2012)Google Scholar
- 45.Tomasi, C., Kanade, T.: Detection and tracking of point features. Carnegie Mellon University Technical Report CMU-CS-91-132 (1991)Google Scholar
- 46.Trifonov, B., Bradley, D., Heidrich, W.: Tomographic reconstruction of transparent objects. In: Proc. Eurographics Symposium on Rendering, pp. 51–60 (2006)Google Scholar
- 47.Vasudeva, G., Honnery, D.R., Soria, J.: Non-intrusive measurement of a density field using the background oriented Schlieren (BOS) method. In: Proc. Australian Conf. Laser Diagnostic in Fluid Mechanics & Combustion (2005)Google Scholar
- 48.Venkatakrishnan, L., Meier, G.E.A.: Density measurements using the background oriented Schlieren technique. Experiments in Fluids 37, 237–247 (2004)CrossRefGoogle Scholar
- 49.Vo, M., Wang, Z., Pan, B., Pan, T.: Hyper-accurate flexible calibration technique for fringe-projection-based three-dimensional imaging. Opt. Express 20(15), 16926–16941 (2012)CrossRefGoogle Scholar
- 50.Wetzstein, G., Raskar, R., Heidrich, W.: Hand-held schlieren photography with light field probes. In: Proc. IEEE ICCP (2011)Google Scholar
- 51.Wetzstein, G., Roodnick, D., Raskar, R., Heidrich, W.: Refractive shape from light field distortion. In: Proc. IEEE ICCV (2011)Google Scholar
- 52.Xue, T., Rubinstein, M., Wadhwa, N., Levin, A., Durand, F., Freeman, W.T.: Refraction wiggles for measuring fluid depth and velocity from video. In: Proc. ECCV (2014)Google Scholar
- 53.Zach, C., Pock, T., Bischof, H.: A duality based approach for realtime TV-L1 optical flow. In: Hamprecht, F.A., Schnörr, C., Jähne, B. (eds.) DAGM 2007. LNCS, vol. 4713, pp. 214–223. Springer, Heidelberg (2007)CrossRefGoogle Scholar
- 54.Zamek, S., Yitzhaky, Y.: Turbulence strength estimation from an arbitrary set of atmospherically degraded images. JOSA A 23(12), 3106–3113 (2006)CrossRefGoogle Scholar
- 55.Zhu, X., Milanfar, P.: Stabilizing and deblurring atmospheric turbulence. In: Proc. IEEE ICCP (2011)Google Scholar