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. 2015 Jul 14;22(Pt 5):1268–1278. doi: 10.1107/S1600577515010176

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© Paleo and Mirone 2015

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Third test case. (a) Original phantom. (b) Result of filtered back-projection after adding lines of variable width and intensity in the sinogram. (c) Image back-projected after applying the Munch et al. de-striper algorithm, with = 2.5, = 5 and the ‘Daubechies 20’ wavelet. (d) Difference between the phantom and the corrected image. The PSNR is 23.2. (e) Result of the correction using the RCP technique, with = 10 and = 10. (f) Difference between the phantom and the corrected image. The PSNR is 21.2. (g) Result of the reconstruction using the Total Variation regularization, with parameters = 0.5, = 0.05. (h) Difference between the phantom and the corrected image. The PSNR is 29.9. (i) Result of the reconstruction using the Dictionary Learning technique with parameters = 0.05, = , = 10. (j) Difference between the reconstruction and the phantom. The PSNR is 28.3.

Inline graphic — Third test case. (a) Original phantom. (b) Result of filtered back-projection after adding lines of variable width and intensity in the sinogram. (c) Image back-projected after applying the Munch et al. de-striper algorithm, with = 2.5, = 5 and the ‘Daubechies 20’ wavelet. (d) Difference between the phantom and the corrected image. The PSNR is 23.2. (e) Result of the correction using the RCP technique, with = 10 and = 10. (f) Difference between the phantom and the corrected image. The PSNR is 21.2. (g) Result of the reconstruction using the Total Variation regularization, with parameters = 0.5, = 0.05. (h) Difference between the phantom and the corrected image. The PSNR is 29.9. (i) Result of the reconstruction using the Dictionary Learning technique with parameters = 0.05, = , = 10. (j) Difference between the reconstruction and the phantom. The PSNR is 28.3.