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Removing Stripes, Scratches, and Curtaining with Nonrecoverable Compressed Sensing

  • Jonathan Schwartz (a1), Yi Jiang (a2), Yongjie Wang (a3), Anthony Aiello (a3), Pallab Bhattacharya (a3), Hui Yuan (a4), Zetian Mi (a3), Nabil Bassim (a5) and Robert Hovden (a1) (a6)...
Abstract

Highly-directional image artifacts such as ion mill curtaining, mechanical scratches, or image striping from beam instability degrade the interpretability of micrographs. These unwanted, aperiodic features extend the image along a primary direction and occupy a small wedge of information in Fourier space. Deleting this wedge of data replaces stripes, scratches, or curtaining, with more complex streaking and blurring artifacts—known within the tomography community as “missing wedge” artifacts. Here, we overcome this problem by recovering the missing region using total variation minimization, which leverages image sparsity-based reconstruction techniques—colloquially referred to as compressed sensing (CS)—to reliably restore images corrupted by stripe-like features. Our approach removes beam instability, ion mill curtaining, mechanical scratches, or any stripe features and remains robust at low signal-to-noise. The success of this approach is achieved by exploiting CS's inability to recover directional structures that are highly localized and missing in Fourier Space.

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Corresponding author
*Author for correspondence: Jonathan Schwartz, E-mail: jtschw@umich.edu
References
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Bender, H, Drijbooms, C & Radisic, A (2011). FIB/SEM structural analysis of through-Silicon-Vias. AIP Conf Proc 1395, 274278.
Bouali, M & Ladjal, S (2011). Toward optimal destriping of MODIS data using a unidirectional variational model. IEEE Trans Geosci Remote Sens 49, 29242935.
Bracewell, RN (1956). Strip integration in radio astronomy. Aust J Phys 9, 198217.
Candes, E, Romberg, J & Tao, T (2008). Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information. IEEE Trans Inf Theory 52, 489509.
Candes, EJ & Wakin, MB (2008). An introduction to compressive sampling. IEEE Signal Proc Mag 25, 2130.
Chen, J, Shao, Y, Guo, H, Wang, W & Zhu, B (2003). Destriping CMODIS data by power filtering. IEEE Trans Geosci Remote Sens 41, 21192124.
Chen, S-WW & Pellequer, J-L (2011). Destripe: Frequency-based algorithm for removing stripe noises from AFM images. BMC Struct Biol 11, 110.
Donoho, DL (2006). Compressed sensing. IEEE Trans Inf Theory 52, 12891306.
Duarte, MF & Eldar, YC (2011). Structured compressed sensing: From theory to applications. IEEE Trans Signal Process 59, 40534085.
Fitschen, JH, Ma, J & Schuff, S (2017). Removal of curtaining effects by a variational model with directional forward differences. Comput Vis Image Underst 155, 2432.
Gadallah, FL, Csillag, F & Smith, EJM (2010). Destriping multisensor imagery with moment matching. Int J Remote Sens 21, 25052511.
Holzer, L, Gasser, PH, Kaech, A, Wegmann, M, Zingg, A, Wepf, R & Muench, B (2007). Cryo-FIB-nanotomography for quantitative analysis of particle structures in cement suspensions. J Microsc 227, 216228.
Jiang, Y, Padgett, E, Hovden, R & Muller, DA (2018). Sampling limits for electron tomography with sparsity-exploiting reconstructions. Ultramicroscopy 186, 94103.
Leary, R, Saghi, Z, Midgley, PA & Holland, DJ (2013). Compressed sensing electron tomography. Ultramicroscopy 131, 7091.
Liang, X, Zang, Y, Dong, D, Zhang, L, Fang, M, Yang, X, Arranz, A, Ripoll, J, Hui, H & Tian, J (2016). Stripe artifact elimination based on nonsubsampled contourlet transform for light sheet fluorescence microscopy. J Biomed Opt 21, 106005106010.
Liu, H, Fang, H, Yan, L & Chang, Y (2013). Robust destriping method with unidirectional total variation and framelet regularization. Opt Express 21, 2330723323.
Lustig, M, Donoho, D & Pauly, JM (2007). Sparse MRI: The application of compressed sensing for rapid MR imaging. Magn Reson Med 58, 11821195.
Midgley, PA & Weyland, M (2003). 3D electron microscopy in the physical sciences: The development of Z-contrast and EFTEM tomography. Ultramicroscopy 96, 413431.
Münch, B, Trtik, P, Marone, F & Stampanoni, M (2009). Stripe and ring artifact removal with combined wavelet—Fourier filtering. Opt Express 17, 85678591.
Natarajan, BK (1995). Sparse approximate solutions to linear systems. SIAM J Comput 24, 227234.
Qi, H, Chen, Z & Zhou, L (2015). CT image reconstruction from sparse projections using adaptive TpV regularization. Comput Math Methods Med 2015, 18.
Rakwatin, P, Takeuchi, W & Yasuoka, Y (2007). Stripe noise reduction in MODIS data by combining histogram matching with facet filter. IEEE Trans Geosci Remote Sens 45, 18441856.
Schankula, CW, Anand, CK & Bassim, ND (2018). Plasma focused ion beam curtaining artifact correction by Fourier-based linear optimization model. Microsc Microanal 24, 588589.
Sidky, EY, Kao, C-M & Pan, X (2006). Accurate image reconstruction from few-views and limited-angle data in divergent-beam CT. J Xray Sci Technol 14, 119139.
Thomas, JM, Leary, R, Midgley, PA & Holland, DJ (2013). A new approach to the investigation of nanoparticles: Electron tomography with compressed sensing. J Colloid Interface Sci 392, 714.
Torres, J & Infante, SO (2001). Wavelet analysis for the elimination of striping noise in satellite images. Opt Eng 40, 13091315.
Zaefferer, S, Wright, SI & Raabe, D (2008). Three-dimensional orientation microscopy in a focused ion beam–scanning electron microscope: a new dimension of microstructure characterization. Metall Mater Trans A 39, 374389.
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Microscopy and Microanalysis
  • ISSN: 1431-9276
  • EISSN: 1435-8115
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