From c9ee9ecc84881595b04f19280c93bcd587171270 Mon Sep 17 00:00:00 2001 From: dkazanc Date: Tue, 4 Dec 2018 16:13:38 +0000 Subject: GPU version, this completes implementation of nltv #68 --- Readme.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) (limited to 'Readme.md') diff --git a/Readme.md b/Readme.md index cc74dbc..01d4586 100644 --- a/Readme.md +++ b/Readme.md @@ -27,7 +27,7 @@ 5. Linear and nonlinear diffusion (explicit PDE minimisation scheme) **2D/3D CPU/GPU** (Ref. *8*) 6. Anisotropic Fourth-Order Diffusion (explicit PDE minimisation) **2D/3D CPU/GPU** (Ref. *9*) 7. A joint ROF-LLT (Lysaker-Lundervold-Tai) model for higher-order regularisation **2D/3D CPU/GPU** (Ref. *10,11*) -8. Nonlocal Total Variation regularisation (GS fixed point iteration) **2D/3D CPU/GPU** (Ref. *12*) +8. Nonlocal Total Variation regularisation (GS fixed point iteration) **2D CPU/GPU** (Ref. *12*) ### Multi-channel (denoising): 1. Fast-Gradient-Projection (FGP) Directional Total Variation **2D/3D CPU/GPU** (Ref. *3,4,2*) -- cgit v1.2.3 From 18f87b2fa294a55616b8f42ae3cac80908b6e694 Mon Sep 17 00:00:00 2001 From: dkazanc Date: Thu, 6 Dec 2018 12:27:51 +0000 Subject: readme updated with figure --- Readme.md | 4 ++++ 1 file changed, 4 insertions(+) (limited to 'Readme.md') diff --git a/Readme.md b/Readme.md index 01d4586..e023ebe 100644 --- a/Readme.md +++ b/Readme.md @@ -10,6 +10,10 @@
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+ ## Prerequisites: * [MATLAB](www.mathworks.com/products/matlab/) OR -- cgit v1.2.3 From eb7166be0c147ff63ebb736480fab35205d995fe Mon Sep 17 00:00:00 2001 From: dkazanc Date: Thu, 6 Dec 2018 12:28:42 +0000 Subject: corrected --- Readme.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) (limited to 'Readme.md') diff --git a/Readme.md b/Readme.md index e023ebe..49cd2b1 100644 --- a/Readme.md +++ b/Readme.md @@ -11,7 +11,7 @@
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## Prerequisites: -- cgit v1.2.3 From 26e847b922f58a87a46982e123ae326c089a7259 Mon Sep 17 00:00:00 2001 From: Daniil Kazantsev Date: Thu, 6 Dec 2018 12:29:34 +0000 Subject: correction2 --- Readme.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) (limited to 'Readme.md') diff --git a/Readme.md b/Readme.md index 49cd2b1..e1d96f5 100644 --- a/Readme.md +++ b/Readme.md @@ -11,7 +11,7 @@
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## Prerequisites: -- cgit v1.2.3 From 9fa6e4fedc4685356467e1d685601e47e6176c9f Mon Sep 17 00:00:00 2001 From: Daniil Kazantsev Date: Thu, 6 Dec 2018 12:31:46 +0000 Subject: rm corr2 --- Readme.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) (limited to 'Readme.md') diff --git a/Readme.md b/Readme.md index e1d96f5..3e3d8c3 100644 --- a/Readme.md +++ b/Readme.md @@ -11,7 +11,7 @@
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## Prerequisites: -- cgit v1.2.3 From 3bce1f1410303a6833d1e647fba9692ea40fa878 Mon Sep 17 00:00:00 2001 From: Daniil Kazantsev Date: Thu, 6 Dec 2018 12:39:13 +0000 Subject: readme update3 --- Readme.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) (limited to 'Readme.md') diff --git a/Readme.md b/Readme.md index 3e3d8c3..089c9fe 100644 --- a/Readme.md +++ b/Readme.md @@ -1,6 +1,6 @@ # CCPi-Regularisation Toolkit (CCPi-RGL) -**Iterative image reconstruction (IIR) methods normally require regularisation to stabilise the convergence and make the reconstruction problem more well-posed. CCPi-RGL software consists of 2D/3D regularisation modules for single-channel and multi-channel reconstruction problems. The regularisation modules are well-suited to use with [splitting algorithms](https://en.wikipedia.org/wiki/Augmented_Lagrangian_method#Alternating_direction_method_of_multipliers), such as ADMM and FISTA. Furthermore, the toolkit can be used independently to solve image denoising and inpaiting tasks. The core modules are written in C-OMP and CUDA languages, wrappers for Matlab and Python are provided.** +**Iterative image reconstruction (IIR) methods normally require regularisation to stabilise the convergence and make the reconstruction problem (inverse problem) more well-posed. The CCPi-RGL software provides 2D/3D and multi-channel regularisation strategies to ensure better performance of IIR methods. The regularisation modules are well-suited to use with [splitting algorithms](https://en.wikipedia.org/wiki/Augmented_Lagrangian_method#Alternating_direction_method_of_multipliers), such as, [ADMM](https://github.com/dkazanc/ADMM-tomo) and [FISTA](https://github.com/dkazanc/FISTA-tomo). Furthermore, the toolkit can be used for simpler inversion tasks, such as, image denoising, inpaiting, deconvolution etc. The core modules are written in C-OMP and CUDA languages and wrappers for Matlab and Python are provided.**

@@ -162,7 +162,7 @@ addpath(/path/to/library); ### Applications: -* [Regularised FISTA iterative reconstruction algorithm for X-ray tomographic reconstruction with highly inaccurate measurements (MATLAB code)](https://github.com/dkazanc/FISTA-tomo) +* [Regularised FISTA iterative reconstruction algorithm for X-ray tomographic reconstruction with highly inaccurate measurements (MATLAB/Python code)](https://github.com/dkazanc/FISTA-tomo) * [Regularised ADMM iterative reconstruction algorithm for X-ray tomographic reconstruction (MATLAB code)](https://github.com/dkazanc/ADMM-tomo) * [Joint image reconstruction method with correlative multi-channel prior for X-ray spectral computed tomography (MATLAB code)](https://github.com/dkazanc/multi-channel-X-ray-CT) -- cgit v1.2.3