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authorDaniil Kazantsev <dkazanc@hotmail.com>2017-07-04 09:35:02 +0100
committerDaniil Kazantsev <dkazanc@hotmail.com>2017-07-04 09:35:02 +0100
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-# FISTA Reconstruction (Daniil Kazanteev)
+# FISTA Reconstruction (Daniil Kazantsev)
# General Description
@@ -14,7 +14,6 @@ Software for reconstructing 2D/3D x-ray and neutron tomography datasets. The dat
### Demos:
* Demo1: Synthetic phantom reconstruction with noise, stripes and zingers
- * Demo2: Synthetic phantom reconstruction with noise, stripes, zingers, and the missing wedges
* DemoRD1: Real data reconstruction from sino_basalt.mat (see Data)
* DemoRD2: Real data reconstruction from sino3D_dendrites.mat (see Data)
@@ -26,7 +25,7 @@ Software for reconstructing 2D/3D x-ray and neutron tomography datasets. The dat
### Main modules:
* FISTA_REC.m – Matlab function to perform FISTA-based reconstruction
- * FISTA_TV.c – C-omp function to solve for the weighted TV term using FISTA
+ * FGP_TV.c – C-omp function to solve for the weighted TV term using FGP
* SplitBregman_TV.c – C-omp function to solve for the weighted TV term using Split-Bregman
* LLT_model.c – C-omp function to solve for the weighted LLT [3] term using explicit scheme
* studentst.m – Matlab function to calculate Students t penalty with 'auto-tuning'
@@ -34,17 +33,13 @@ Software for reconstructing 2D/3D x-ray and neutron tomography datasets. The dat
### Supplementary:
* zing_rings_add.m Matlab script to generate proj. data, add noise, zingers and stripes
- * add_wedges.m script to add the missing wedge to existing sinogram
- * my_red_yellowMAP.mat – nice colormap for the phantom
+ * my_red_yellowMAP.mat – nice colormap for the phantom
* RMSE.m – Matlab function to calculate Root Mean Square Error
- * subplot_tight – visualizing better subplots
- * ssim_index – ssim calculation
-
+
### Practical advices:
* Full 3D reconstruction provides much better results than 2D. In the case of ring artifacts, 3D is almost necessary
* Depending on data it is better to use TV-LLT combination in order to achieve piecewise-smooth solution. The DemoRD2 shows one possible example when smoother surfaces required.
* L (Lipshitz constant) if tweaked can lead to faster convergence than automatic values
- * Convergence is normally much faster when using Fourier filtering before backprojection
* Students’t penalty is generally quite stable in practice, however some tweaking of L might require for the real data
* You can choose between SplitBregman-TV and FISTA-TV modules. The former is slower but requires less memory (for 3D volume U it can take up to 6 x U), the latter is faster but can take more memory (for 3D volume U it can take up to 11 x U). Also the SplitBregman is quite good in improving contrast.