image destriping
{{short description|Image processing task}}
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,{{cite journal |last1=Chen |first1=S. W. |last2=Pellequer |first2=J. L. |date=2011 |title=DeStripe: frequency-based algorithm for removing stripe noises from AFM images. |journal= BMC Structural Biology |volume=11 |pages=7|doi=10.1186/1472-6807-11-7 |pmid=21281524 |pmc=3749244 |doi-access=free }} light sheet fluorescence microscopy,{{cite journal |last1=Liang |first1=X. |last2=Zang |first2=Y. |last3=Dong |first3=D. | last4=Zhang |first4=L.|last5=Fang |first5=M. |last6=Arranz |first6=A. |last7=Ripoll |first7=J. |last8=Hui |first8=H. |last9=Tian |first9=J. |date=2016 |title=Stripe artifact elimination based on nonsubsampled contourlet transform for light sheet fluorescence microscopy. |journal= Journal of Biomedical Optics |volume=21 |issue=10 |pages=106005–106010 |doi=10.1117/1.jbo.21.10.106005|pmid=27784051 |bibcode=2016JBO....21j6005L |doi-access=free }} and planetary satellite imaging.{{cite journal |last1=Rakwatin |first1=P. |last2=Takeuchi |first2=W. |last3=Yasuoka |first3=Y. |date=2007 |title=Stripe Noise Reduction in MODIS Data by Combining Histogram Matching With Facet Filter. |journal= IEEE Transactions on Geoscience and Remote Sensing |volume=45 |issue=6 |pages=1844–1856 |doi=10.1109/tgrs.2007.895841|bibcode=2007ITGRS..45.1844R |s2cid=9046902 }}
The most common image processing techniques to reduce stripe artifacts is with Fourier filtering.{{cite journal |last1=Chen |first1=J. |last2=Shao |first2=Y |last3=Guo |first3=H. |last4=Wang |first4=W. |last5=Zhu |first5=B. |date=2003 |title=Destriping CMODIS data by power filtering. |journal=IEEE Trans Geosci Remote Sens |volume=41 |issue=9 |pages=2119–2124 |doi=10.1109/tgrs.2003.817206|bibcode=2003ITGRS..41.2119C }} 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.{{cite journal |last1=Gadallah |first1=F.L. |last2=Csillag |first2=F |last3=Smith |first3=E.J.M. |date=2010 |title=Destriping multisensor imagery with moment matching. |journal= Int J Remote Sens |volume=21 |issue=12 |pages=2505–2511|doi=10.1080/01431160050030592 |s2cid=128408378 }} More recently, a new class of approaches leverage compressed sensing, to regularize an optimization problem, and recover stripe free images.{{cite journal |last1=Fitschen |first1=J.H. |last2=Ma |first2=J |last3=Schuff |first3=S. |date=2017 |title=Removal of curtaining effects by a variational model with directional forward differences. |journal= Comput Vis Image Underst |volume=155 |pages=24–32 |doi=10.1016/j.cviu.2016.12.008|arxiv=1507.00112 |s2cid=5224151 }}{{cite journal |last1=Schwartz |first1=J. |last2=Jiang |first2=Y |last3=Bassim |first3=N. |last4=Hovden |first4=R. |date=2019 |title=Removing Stripes, Scratches, and Curtaining with Nonrecoverable Compressed Sensing. |journal=Microscopy and Microanalysis|volume=25 |issue=3 |pages=705–710 |pmid=30867078 |arxiv=1901.08001 |bibcode=2019MiMic..25..705S |doi=10.1017/S1431927619000254 |s2cid=59158809 }}{{Cite journal|last1=Bouali|first1=Marouan|last2=Ladjal|first2=Saïd|date=August 2011|title=Toward Optimal Destriping of MODIS Data Using a Unidirectional Variational Model|journal=IEEE Transactions on Geoscience and Remote Sensing|volume=49|issue=8|pages=2924–2935|doi=10.1109/TGRS.2011.2119399|bibcode=2011ITGRS..49.2924B|s2cid=14902535 |issn=0196-2892}} In many cases, these destriped images have little to no artifacts, even at low signal to noise ratios.