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| author | Daniil Kazantsev <dkazanc@hotmail.com> | 2019-03-20 22:28:28 +0000 |
|---|---|---|
| committer | Daniil Kazantsev <dkazanc@hotmail.com> | 2019-03-20 22:28:28 +0000 |
| commit | 98febcfe2112c9f00bd25352ef6ba66e7a95e48b (patch) | |
| tree | 76d7bfcf733429c33eca8fe72039ae697b57e1eb /Readme.md | |
| parent | fab130f7dcf5074080cc2a9fd96696c0f1ceea21 (diff) | |
| download | regularization-98febcfe2112c9f00bd25352ef6ba66e7a95e48b.tar.gz regularization-98febcfe2112c9f00bd25352ef6ba66e7a95e48b.tar.bz2 regularization-98febcfe2112c9f00bd25352ef6ba66e7a95e48b.tar.xz regularization-98febcfe2112c9f00bd25352ef6ba66e7a95e48b.zip | |
readme update2
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| -rw-r--r-- | Readme.md | 4 |
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@@ -6,10 +6,6 @@ **Iterative image reconstruction (IIR) methods frequently require regularisation to ensure convergence and make inverse problem well-posed. The CCPi-RGL toolkit provides a set of 2D/3D regularisation strategies to guarantee a better performance of IIR methods (higher SNR and resolution). The regularisation modules for scalar and vectorial datasets are based on the [proximal operator](https://en.wikipedia.org/wiki/Proximal_operator) framework and can be used with [proximal splitting algorithms](https://en.wikipedia.org/wiki/Proximal_gradient_method), such as PDHG, Douglas-Rachford, ADMM, FISTA and [others](https://arxiv.org/abs/0912.3522). While the main target for CCPi-RGL is [tomographic image reconstruction](https://github.com/dkazanc/TomoRec), the toolkit can be used for image denoising and inpaiting problems. The core modules are written in C-OMP and CUDA languages and wrappers for Matlab and Python are provided.** - -<div align="center"> - <img src="demos/images/probl.png" height="225"><br> -</div> <div align="center"> <img src="demos/images/CCPiRGL_sm.jpg" height="400"><br> </div> |