The Matlab code can be downloaded from GitHub:
git clone https://github.com/JanSochman/TVdenoising.git
Feel free to use it.
Implemented total variational models for denoising
- ROF model (preserves discontinuities due to L1 norm) $$ \min_{u\in X} ||\nabla u||_1 + \frac{\lambda}{2}||u - g||_2^2 $$
- TV-L1 ROF (adds robustness to noise outliers) $$ \min_{u\in X} ||\nabla u||_1 + \lambda||u - g||_1 $$
- Huber-ROF (avoids staircasing effect of TV methods) $$ \min_{u\in X} |\nabla u|_\alpha + \frac{\lambda}{2}||u - g||_2^2 \quad \quad |x|_\alpha = \left\{\begin{array}{lr} |x|^2/(2\alpha), \, & |x| \leq \alpha\\ |x| - \alpha/2, \, & |x| > \alpha \end{array} \right. $$
Where $g$ is the input image, $u$ the sought solution, and $X$ is the set of $M\times N$ images.
The optimisation
- All problems are convex but only Huber-ROF is smooth
- All problems can be expressed as $$ \min_{x\in X} F(Kx) + G(x) $$ with corresponding primal-dual saddle-point problem being $$ \min_{x\in X} \max_{y\in Y} \langle Kx,y\rangle + G(x) - F^{*}(y) $$
- This can be solved by an iterative primal-dual algorithm (see the paper)
- Initialisation: Choose $\tau$, $\sigma \gt 0$, $\theta \in [0, 1]$, $(x^0,y^0)\in X\times Y$ and set $\bar{x}^0 = x^0$.
- Iterations ($n\ge 0$): Update $x^n$, $y^n$, $\bar{x}^n$ as follows: $$\left\{ \begin{array}{l} y^{n+1} = (I + \sigma\partial F^*)^{-1}(y^n + \sigma K \bar{x}^n)\\ x^{n+1} = (I + \tau \partial G)^{-1}(x^n-\tau K^*y^{n+1})\\ \bar{x}^{n+1} = x^{n+1} + \theta (x^{n+1}-x^n)\end{array}\right.$$
- Two more variants with faster convergence proposed in the paper in case $G$ or/and $F^*$ are uniformly convex (used when applicable)
Methods comparison: salt & pepper noise
| original image | salt & pepper noise | ROF (non-robust) |
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| TV-L1 ROF (robust but staircasing) |
Huber ROF (smooth but non-robust) |
Huber-L1 ROF (smooth and robust) |
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Methods comparison: gaussian noise
| original image | gaussian noise | ROF (non-robust) |
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| TV-L1 ROF (robust but staircasing) |
Huber ROF (smooth but non-robust) |
Huber-L1 ROF (smooth and robust) |
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