3-D Discrete Shearlet Transform and Video Processing
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In this paper, we introduce a digital implementation of the 3-D shearlet transform and illustrate its application to problems of video denoising and enhancement. The shearlet representation is a multiscale pyramid of well-localized waveforms defined at various locations and orientations, which was introduced to overcome the limitations of traditional multiscale systems in dealing with multidimensional data. While the shearlet approach shares the general philosophy of curvelets and surfacelets, it is based on a very different mathematical framework, which is derived from the theory of affine systems and uses shearing matrices rather than rotations. This allows a natural transition from the continuous setting to the digital setting and a more flexible mathematical structure. The 3-D digital shearlet transform algorithm presented in this paper consists in a cascade of a multiscale decomposition and a directional filtering stage. The filters employed in this decomposition are implemented as finite-length filters, and this ensures that the transform is local and numerically efficient. To illustrate its performance, the 3-D discrete shearlet transform is applied to problems of video denoising and enhancement, and compared against other state-of-the-art multiscale techniques, including curvelets and surfacelets.
Esmaeili M, Dehnavi A, Hajizadeh F, Rabbani H Biomed Opt Express. 2020; 11(2):586-608.
PMID: 32133216 PMC: 7041443. DOI: 10.1364/BOE.377021.
Baghaie A, Schnell S, Bakhshinejad A, Fathi M, DSouza R, Rayz V Comput Biol Med. 2018; 99:142-153.
PMID: 29929053 PMC: 6077095. DOI: 10.1016/j.compbiomed.2018.06.008.
Kayasandik C, Negi P, Laezza F, Papadakis M, Labate D Sci Rep. 2018; 8(1):6450.
PMID: 29691458 PMC: 5915526. DOI: 10.1038/s41598-018-24753-w.
Automatic Image Registration of Multi-Modal Remotely Sensed Data with Global Shearlet Features.
Murphy J, Le Moigne J, Harding D IEEE Trans Geosci Remote Sens. 2017; 54(3):1685-1704.
PMID: 29123329 PMC: 5674534. DOI: 10.1109/TGRS.2015.2487457.
Automated detection of soma location and morphology in neuronal network cultures.
Ozcan B, Negi P, Laezza F, Papadakis M, Labate D PLoS One. 2015; 10(4):e0121886.
PMID: 25853656 PMC: 4390318. DOI: 10.1371/journal.pone.0121886.