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Image destriping

Summary

Image destriping is the process of removing stripes or streaks from images and videos without disrupting the original image/video. These artifacts plague a range of fields in scientific imaging including atomic force microscopy,[2] light sheet fluorescence microscopy,[3] and planetary satellite imaging.[4]

Image destriping using the Schwartz-Hovden destripe algorithm.[1] Scale bar 2 μm.

The most common image processing techniques to reduce stripe artifacts is with Fourier filtering.[5] Unfortunately, filtering methods risk altering or suppressing useful image data. Methods developed for multiple-sensor imaging systems in planetary satellites use statistical-based methods to match signal distribution across multiple sensors.[6] More recently, a new class of approaches leverage compressed sensing, to regularize an optimization problem, and recover stripe free images.[7][1][8] In many cases, these destriped images have little to no artifacts, even at low signal to noise ratios.[1]

References edit

  1. ^ a b c Schwartz, J.; Jiang, Y; Bassim, N.; Hovden, R. (2019). "Removing Stripes, Scratches, and Curtaining with Nonrecoverable Compressed Sensing". Microscopy and Microanalysis. 25 (3): 705–710. arXiv:1901.08001. Bibcode:2019MiMic..25..705S. doi:10.1017/S1431927619000254. PMID 30867078. S2CID 59158809.
  2. ^ Chen, S. W.; Pellequer, J. L. (2011). "DeStripe: frequency-based algorithm for removing stripe noises from AFM images". BMC Structural Biology. 11: 7. doi:10.1186/1472-6807-11-7. PMC 3749244. PMID 21281524.
  3. ^ Liang, X.; Zang, Y.; Dong, D.; Zhang, L.; Fang, M.; Arranz, A.; Ripoll, J.; Hui, H.; Tian, J. (2016). "Stripe artifact elimination based on nonsubsampled contourlet transform for light sheet fluorescence microscopy". Journal of Biomedical Optics. 21 (10): 106005–106010. Bibcode:2016JBO....21j6005L. doi:10.1117/1.jbo.21.10.106005. PMID 27784051.
  4. ^ Rakwatin, P.; Takeuchi, W.; Yasuoka, Y. (2007). "Stripe Noise Reduction in MODIS Data by Combining Histogram Matching With Facet Filter". IEEE Transactions on Geoscience and Remote Sensing. 45 (6): 1844–1856. Bibcode:2007ITGRS..45.1844R. doi:10.1109/tgrs.2007.895841. S2CID 9046902.
  5. ^ Chen, J.; Shao, Y; Guo, H.; Wang, W.; Zhu, B. (2003). "Destriping CMODIS data by power filtering". IEEE Trans Geosci Remote Sens. 41 (9): 2119–2124. Bibcode:2003ITGRS..41.2119C. doi:10.1109/tgrs.2003.817206.
  6. ^ Gadallah, F.L.; Csillag, F; Smith, E.J.M. (2010). "Destriping multisensor imagery with moment matching". Int J Remote Sens. 21 (12): 2505–2511. doi:10.1080/01431160050030592. S2CID 128408378.
  7. ^ Fitschen, J.H.; Ma, J; Schuff, S. (2017). "Removal of curtaining effects by a variational model with directional forward differences". Comput Vis Image Underst. 155: 24–32. arXiv:1507.00112. doi:10.1016/j.cviu.2016.12.008. S2CID 5224151.
  8. ^ Bouali, Marouan; Ladjal, Saïd (August 2011). "Toward Optimal Destriping of MODIS Data Using a Unidirectional Variational Model". IEEE Transactions on Geoscience and Remote Sensing. 49 (8): 2924–2935. Bibcode:2011ITGRS..49.2924B. doi:10.1109/TGRS.2011.2119399. ISSN 0196-2892. S2CID 14902535.