readme upd7

1 files changed, 18 insertions, 13 deletions

diff --git a/Readme.md b/Readme.md
index 018808c..e73b4fb 100644
--- a/Readme.md
+++ b/Readme.md
@@ -11,7 +11,7 @@ the toolkit can be used independently to solve image denoising problems. The cor
 
 ## Prerequisites: 
 
- * MATLAB (www.mathworks.com/products/matlab/) OR
+ * [MATLAB](www.mathworks.com/products/matlab/) OR
  * Python (tested ver. 3.5); Cython
  * C compilers
  * nvcc (CUDA SDK) compilers
@@ -19,14 +19,14 @@ the toolkit can be used independently to solve image denoising problems. The cor
 ## Package modules (regularisers):
 
 ### Single-channel
-1. Rudin-Osher-Fatemi (ROF) Total Variation (explicit PDE minimisation scheme) [2D/3D CPU/GPU]; (Ref. 1)
-2. Fast-Gradient-Projection (FGP) Total Variation [2D/3D CPU/GPU]; (Ref. 2)
-3. Split-Bregman (SB) Total Variation [2D/3D CPU/GPU]; (Ref. 4)
-4. Linear and nonlinear diffusion (explicit PDE minimisation scheme) [2D/3D CPU/GPU]; (Ref. 6)
+1. Rudin-Osher-Fatemi (ROF) Total Variation (explicit PDE minimisation scheme) **2D/3D CPU/GPU** (Ref. *1*)
+2. Fast-Gradient-Projection (FGP) Total Variation **2D/3D CPU/GPU** (Ref. *2*)
+3. Split-Bregman (SB) Total Variation **2D/3D CPU/GPU** (Ref. *4*)
+4. Linear and nonlinear diffusion (explicit PDE minimisation scheme) **2D/3D CPU/GPU** (Ref. *6*)
 
 ### Multi-channel
-1. Fast-Gradient-Projection (FGP) Directional Total Variation [2D/3D CPU/GPU]; (Ref. 3,2)
-2. Total Nuclear Variation (TNV) penalty [2D+channels CPU]; (Ref. 5)
+1. Fast-Gradient-Projection (FGP) Directional Total Variation **2D/3D CPU/GPU** (Ref. *3,2*)
+2. Total Nuclear Variation (TNV) penalty **2D+channels CPU** (Ref. *5*)
 
 ## Installation:
 
@@ -50,12 +50,17 @@ the toolkit can be used independently to solve image denoising problems. The cor
 ```
 
 ### References:
-1. Rudin, L.I., Osher, S. and Fatemi, E., 1992. Nonlinear total variation based noise removal algorithms. Physica D: nonlinear phenomena, 60(1-4), pp.259-268.
-2. Beck, A. and Teboulle, M., 2009. Fast gradient-based algorithms for constrained total variation image denoising and deblurring problems. IEEE Transactions on Image Processing, 18(11), pp.2419-2434.
-3. Ehrhardt, M.J. and Betcke, M.M., 2016. Multicontrast MRI reconstruction with structure-guided total variation. SIAM Journal on Imaging Sciences, 9(3), pp.1084-1106.
-4. Goldstein, T. and Osher, S., 2009. The split Bregman method for L1-regularized problems. SIAM journal on imaging sciences, 2(2), pp.323-343.
-5. Duran, J., Moeller, M., Sbert, C. and Cremers, D., 2016. Collaborative total variation: a general framework for vectorial TV models. SIAM Journal on Imaging Sciences, 9(1), pp.116-151.
-6. Black, M.J., Sapiro, G., Marimont, D.H. and Heeger, D., 1998. Robust anisotropic diffusion. IEEE Transactions on image processing, 7(3), pp.421-432.
+*1. Rudin, L.I., Osher, S. and Fatemi, E., 1992. Nonlinear total variation based noise removal algorithms. Physica D: nonlinear phenomena, 60(1-4), pp.259-268.*
+
+*2. Beck, A. and Teboulle, M., 2009. Fast gradient-based algorithms for constrained total variation image denoising and deblurring problems. IEEE Transactions on Image Processing, 18(11), pp.2419-2434.*
+
+*3. Ehrhardt, M.J. and Betcke, M.M., 2016. Multicontrast MRI reconstruction with structure-guided total variation. SIAM Journal on Imaging Sciences, 9(3), pp.1084-1106.*
+
+*4. Goldstein, T. and Osher, S., 2009. The split Bregman method for L1-regularized problems. SIAM journal on imaging sciences, 2(2), pp.323-343.*
+
+*5. Duran, J., Moeller, M., Sbert, C. and Cremers, D., 2016. Collaborative total variation: a general framework for vectorial TV models. SIAM Journal on Imaging Sciences, 9(1), pp.116-151.*
+
+*6. Black, M.J., Sapiro, G., Marimont, D.H. and Heeger, D., 1998. Robust anisotropic diffusion. IEEE Transactions on image processing, 7(3), pp.421-432.*
 
 ### License:
 [Apache License, Version 2.0](http://www.apache.org/licenses/LICENSE-2.0)