Super-resolution of turbulent passive scalar images using data assimilation
In this paper, the problem of improving the quality of low-resolution passive scalar image sequences is addressed. This situation, known as “image super-resolution” in computer vision, aroused to our knowledge very few applications in the field of fluid visualization. Yet, in most image acquisition devices, the spatial resolution of the acquired data is limited by the sensor physical properties, while users often require higher-resolution images for further processing and analysis of the system of interest. The originality of the approach presented in this paper is to link the image super-resolution process together with the large eddy simulation framework in order to derive a complete super-resolution technique. We first start by defining two categories of fine-scale components we aim to reconstruct. Then, using a deconvolution procedure as well as data assimilation tools, we show how to partially recover some of these missing components within the low-resolution images while ensuring the temporal consistency of the solution. This method is evaluated using both synthetic and real image data. Finally, we demonstrate how the produced high-resolution images can improve a posteriori analysis such as motion field estimation.
KeywordsScalar Field Direct Numerical Simulation Motion Field Average Angular Error Image Acquisition Device
This work is supported by CaiYuanPei EGIDE grant.
- Borman S, Stevenson RL (1998) Super-resolution from image sequences—a review. In: Proceedings of the 1998 midwest symposium on circuits and systemsGoogle Scholar
- Lions J (1971) Optimal control of systems governed by PDEs. Springer, NewYorkGoogle Scholar
- Lucas BD, Kanade T (1981) An iterative image registration technique with an application to stereo vision. IJCAI 81:674–679Google Scholar
- Papadakis N, Corpetti T, Mémin E (2007) Dynamically consistent optical flow estimation. In: IEEE 11th international conference on computer vision (ICCV’07). Rio de Janeiro, Brazil, pp 1–7. doi: 10.1109/ICCV.2007.4408889
- Rabaud V, Belongie S (2005) Big little icons. CVPR 3:24–30Google Scholar
- Sagaut P (2006a) Large eddy simulation for incompressible flows. Springer editionGoogle Scholar
- Su LK, Dahm WJA (1996a) Scalar imaging velocimetry measurements of the velocity gradient tensor field in turbulent flows. I. Assessment of errors. In: Proceedings of imaging understanding workshopGoogle Scholar
- Sun D, Roth S, Darmstadt T (2010) Secrets of optical flow estimation and their principles. In: Proceedings of 2010 IEEE computer vision and pattern recognition, pp 2432–2439Google Scholar
- Sun J, Xu Z, Shum H-Y (2008) Image super-resolution using gradient profile prior. In: CVPR. IEEE Computer Society. ISBN 978-1-4244-2242-5. http://dblp.uni-trier.de/db/conf/cvpr/cvpr2008.html#SunXS08
- Wen S, Yan L, Feng W, Shipeng L (2009) Real-time screen image scaling and its GPU acceleration. In: ICIP. IEEE, pp 3285–3288. ISBN 978-1-4244-5654-3. http://dblp.uni-trier.de/db/conf/icip/icip2009.html#SunLWL09a