readme updates and demos

2 files changed, 20 insertions, 14 deletions

diff --git a/demos/SoftwareX_supp/Demo_RealData_Recon_SX.py b/demos/SoftwareX_supp/Demo_RealData_Recon_SX.py
index ca8f1d2..5991989 100644
--- a/demos/SoftwareX_supp/Demo_RealData_Recon_SX.py
+++ b/demos/SoftwareX_supp/Demo_RealData_Recon_SX.py
@@ -1,15 +1,15 @@
 #!/usr/bin/env python3
 # -*- coding: utf-8 -*-
 """
-This demo scripts support the following publication: 
-"CCPi-Regularisation Toolkit for computed tomographic image reconstruction with 
+This demo scripts support the following publication:
+"CCPi-Regularisation Toolkit for computed tomographic image reconstruction with
 proximal splitting algorithms" by Daniil Kazantsev, Edoardo Pasca, Martin J. Turner,
  Philip J. Withers; Software X, 2019
 ____________________________________________________________________________
 * Reads real tomographic data (stored at Zenodo)
 --- https://doi.org/10.5281/zenodo.2578893
 * Reconstructs using TomoRec software
-* Saves reconstructed images 
+* Saves reconstructed images
 ____________________________________________________________________________
 >>>>> Dependencies: <<<<<
 1. ASTRA toolbox: conda install -c astra-toolbox astra-toolbox
@@ -40,7 +40,7 @@ data_norm = normaliser(dataRaw, flats, darks, log='log')
 del dataRaw, darks, flats
 
 intens_max = 2.3
-plt.figure() 
+plt.figure()
 plt.subplot(131)
 plt.imshow(data_norm[:,150,:],vmin=0, vmax=intens_max)
 plt.title('2D Projection (analytical)')
@@ -72,7 +72,7 @@ FBPrec = RectoolsDIR.FBP(data_norm[0:100,:,det_y_crop])
 
 sliceSel = 50
 max_val = 0.003
-plt.figure() 
+plt.figure()
 plt.subplot(131)
 plt.imshow(FBPrec[sliceSel,:,:],vmin=0, vmax=max_val, cmap="gray")
 plt.title('FBP Reconstruction, axial view')
@@ -108,7 +108,7 @@ RectoolsIR = RecToolsIR(DetectorsDimH =  np.size(det_y_crop),  # DetectorsDimH #
                     DetectorsDimV = 100,  # DetectorsDimV # detector dimension (vertical) for 3D case only
                     AnglesVec = angles_rad, # array of angles in radians
                     ObjSize = N_size, # a scalar to define reconstructed object dimensions
-                    datafidelity='LS',# data fidelity, choose LS, PWLS (wip), GH (wip), Student (wip)
+                    datafidelity='LS',# data fidelity, choose LS, PWLS, GH (wip), Students t (wip)
                     nonnegativity='ENABLE', # enable nonnegativity constraint (set to 'ENABLE')
                     OS_number = None, # the number of subsets, NONE/(or > 1) ~ classical / ordered subsets
                     tolerance = 0.0, # tolerance to stop inner (regularisation) iterations earlier
@@ -124,7 +124,7 @@ RecADMM_reg_sbtv = RectoolsIR.ADMM(data_norm[0:100,:,det_y_crop],
 
 sliceSel = 50
 max_val = 0.003
-plt.figure() 
+plt.figure()
 plt.subplot(131)
 plt.imshow(RecADMM_reg_sbtv[sliceSel,:,:],vmin=0, vmax=max_val, cmap="gray")
 plt.title('3D ADMM-SB-TV Reconstruction, axial view')
@@ -164,7 +164,7 @@ RecADMM_reg_rofllt = RectoolsIR.ADMM(data_norm[0:100,:,det_y_crop],
 
 sliceSel = 50
 max_val = 0.003
-plt.figure() 
+plt.figure()
 plt.subplot(131)
 plt.imshow(RecADMM_reg_rofllt[sliceSel,:,:],vmin=0, vmax=max_val)
 plt.title('3D ADMM-ROFLLT Reconstruction, axial view')
@@ -202,7 +202,7 @@ RecADMM_reg_tgv = RectoolsIR.ADMM(data_norm[0:100,:,det_y_crop],
 
 sliceSel = 50
 max_val = 0.003
-plt.figure() 
+plt.figure()
 plt.subplot(131)
 plt.imshow(RecADMM_reg_tgv[sliceSel,:,:],vmin=0, vmax=max_val)
 plt.title('3D ADMM-TGV Reconstruction, axial view')
diff --git a/demos/demoMatlab_3Ddenoise.m b/demos/demoMatlab_3Ddenoise.m
index ec0fd88..6b21e86 100644
--- a/demos/demoMatlab_3Ddenoise.m
+++ b/demos/demoMatlab_3Ddenoise.m
@@ -18,9 +18,10 @@ Ideal3D(:,:,i) = Im;
 end
 vol3D(vol3D < 0) = 0;
 figure; imshow(vol3D(:,:,7), [0 1]); title('Noisy image');
-lambda_reg = 0.03; % regularsation parameter for all methods
+
 %%
 fprintf('Denoise a volume using the ROF-TV model (CPU) \n');
+lambda_reg = 0.03; % regularsation parameter for all methods
 tau_rof = 0.0025; % time-marching constant 
 iter_rof = 300; % number of ROF iterations
 epsil_tol =  0.0; % tolerance
@@ -31,14 +32,17 @@ fprintf('%s %f \n', 'RMSE error for ROF is:', rmse_rof);
 figure; imshow(u_rof(:,:,7), [0 1]); title('ROF-TV denoised volume (CPU)');
 %%
 % fprintf('Denoise a volume using the ROF-TV model (GPU) \n');
+% lambda_reg = 0.03; % regularsation parameter for all methods
 % tau_rof = 0.0025; % time-marching constant 
 % iter_rof = 300; % number of ROF iterations
-% tic; u_rofG = ROF_TV_GPU(single(vol3D), lambda_reg, iter_rof, tau_rof); toc;
+% epsil_tol =  0.0; % tolerance
+% tic; u_rofG = ROF_TV_GPU(single(vol3D), lambda_reg, iter_rof, tau_rof, epsil_tol); toc;
 % rmse_rofG = (RMSE(Ideal3D(:),u_rofG(:)));
 % fprintf('%s %f \n', 'RMSE error for ROF is:', rmse_rofG);
 % figure; imshow(u_rofG(:,:,7), [0 1]); title('ROF-TV denoised volume (GPU)');
 %%
 fprintf('Denoise a volume using the FGP-TV model (CPU) \n');
+lambda_reg = 0.03; % regularsation parameter for all methods
 iter_fgp = 300; % number of FGP iterations
 epsil_tol =  0.0; % tolerance
 tic; [u_fgp,infovec] = FGP_TV(single(vol3D), lambda_reg, iter_fgp, epsil_tol); toc; 
@@ -47,9 +51,10 @@ rmse_fgp = (RMSE(Ideal3D(:),u_fgp(:)));
 fprintf('%s %f \n', 'RMSE error for FGP-TV is:', rmse_fgp);
 figure; imshow(u_fgp(:,:,7), [0 1]); title('FGP-TV denoised volume (CPU)');
 %%
-% fprintf('Denoise a volume using the FGP-TV model (GPU) \n');
+fprintf('Denoise a volume using the FGP-TV model (GPU) \n');
+% lambda_reg = 0.03; % regularsation parameter for all methods
 % iter_fgp = 300; % number of FGP iterations
-% epsil_tol =  1.0e-05; % tolerance
+% epsil_tol =  0.0; % tolerance
 % tic; u_fgpG = FGP_TV_GPU(single(vol3D), lambda_reg, iter_fgp, epsil_tol); toc; 
 % rmse_fgpG = (RMSE(Ideal3D(:),u_fgpG(:)));
 % fprintf('%s %f \n', 'RMSE error for FGP-TV is:', rmse_fgpG);
@@ -66,7 +71,7 @@ figure; imshow(u_sb(:,:,7), [0 1]); title('SB-TV denoised volume (CPU)');
 %%
 % fprintf('Denoise a volume using the SB-TV model (GPU) \n');
 % iter_sb = 150; % number of SB iterations
-% epsil_tol =  1.0e-05; % tolerance
+% epsil_tol =  0.0; % tolerance
 % tic; u_sbG = SB_TV_GPU(single(vol3D), lambda_reg, iter_sb, epsil_tol); toc; 
 % rmse_sbG = (RMSE(Ideal3D(:),u_sbG(:)));
 % fprintf('%s %f \n', 'RMSE error for SB-TV is:', rmse_sbG);
@@ -88,6 +93,7 @@ figure; imshow(u_rof_llt(:,:,7), [0 1]); title('ROF-LLT denoised volume (CPU)');
 % lambda_LLT = lambda_reg*0.35; % LLT regularisation parameter
 % iter_LLT = 300; % iterations 
 % tau_rof_llt = 0.0025; % time-marching constant 
+% epsil_tol =  0.0; % tolerance
 % tic; u_rof_llt_g = LLT_ROF_GPU(single(vol3D), lambda_ROF, lambda_LLT, iter_LLT, tau_rof_llt, epsil_tol); toc; 
 % rmse_rof_llt = (RMSE(Ideal3D(:),u_rof_llt_g(:)));
 % fprintf('%s %f \n', 'RMSE error for ROF-LLT is:', rmse_rof_llt);