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filtering. 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. More recently, a new class of approaches
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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
58:, to regularize an optimization problem, and recover stripe free images. In many cases, these destriped images have little to no artifacts, even at low signal to noise ratios.
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Schwartz, J.; Jiang, Y; Bassim, N.; Hovden, R. (2019). "Removing
Stripes, Scratches, and Curtaining with Nonrecoverable Compressed Sensing".
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Rakwatin, P.; Takeuchi, W.; Yasuoka, Y. (2007). "Stripe Noise
Reduction in MODIS Data by Combining Histogram Matching With Facet Filter".
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Fitschen, J.H.; Ma, J; Schuff, S. (2017). "Removal of curtaining effects by a variational model with directional forward differences".
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Bouali, Marouan; Ladjal, Saïd (August 2011). "Toward
Optimal Destriping of MODIS Data Using a Unidirectional Variational Model".
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194:"Stripe artifact elimination based on nonsubsampled contourlet transform for light sheet fluorescence microscopy"
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Gadallah, F.L.; Csillag, F; Smith, E.J.M. (2010). "Destriping multisensor imagery with moment matching".
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Liang, X.; Zang, Y.; Dong, D.; Zhang, L.; Fang, M.; Arranz, A.; Ripoll, J.; Hui, H.; Tian, J. (2016).
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Chen, J.; Shao, Y; Guo, H.; Wang, W.; Zhu, B. (2003). "Destriping CMODIS data by power filtering".
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Image destriping using the
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