Total variation denoising

Example of application of the Rudin et al.^[1] total variation denoising technique to an image corrupted by Gaussian noise. This example created using demo_tv.m by Guy Gilboa, see external links.

In signal processing, particularly image processing, total variation denoising, also known as total variation regularization or total variation filtering, is a noise removal process (filter). It is based on the principle that signals with excessive and possibly spurious detail have high total variation, that is, the integral of the image gradient magnitude is high. According to this principle, reducing the total variation of the signal—subject to it being a close match to the original signal—removes unwanted detail whilst preserving important details such as edges. The concept was pioneered by L. I. Rudin, S. Osher, and E. Fatemi in 1992 and so is today known as the ROF model.^[1]

This noise removal technique has advantages over simple techniques such as linear smoothing or median filtering which reduce noise but at the same time smooth away edges to a greater or lesser degree. By contrast, total variation denoising is a remarkably effective edge-preserving filter, i.e., simultaneously preserving edges whilst smoothing away noise in flat regions, even at low signal-to-noise ratios.^[2]

1D signal series

Application of 1D total-variation denoising to a signal obtained from a single-molecule experiment.^[3] Gray is the original signal, black is the denoised signal.

For a digital signal ${\displaystyle x_{n}}$, we can, for example, define the total variation as

${\displaystyle V(x)=\sum _{n}|x_{n+1}-x_{n}|.}$

Given an input signal ${\displaystyle x_{n}}$, the goal of total variation denoising is to find an approximation, call it ${\displaystyle y_{n}}$, that has smaller total variation than ${\displaystyle x_{n}}$ but is "close" to ${\displaystyle x_{n}}$. One measure of closeness is the sum of square errors:

${\displaystyle \operatorname {E} (x,y)={\frac {1}{n}}\sum _{n}(x_{n}-y_{n})^{2}.}$

So the total-variation denoising problem amounts to minimizing the following discrete functional over the signal ${\displaystyle y_{n}}$:

${\displaystyle \operatorname {E} (x,y)+\lambda V(y).}$

By differentiating this functional with respect to ${\displaystyle y_{n}}$, we can derive a corresponding Euler–Lagrange equation, that can be numerically integrated with the original signal ${\displaystyle x_{n}}$ as initial condition. This was the original approach.^[1] Alternatively, since this is a convex functional, techniques from convex optimization can be used to minimize it and find the solution ${\displaystyle y_{n}}$.^[3]

Regularization properties

The regularization parameter ${\displaystyle \lambda }$ plays a critical role in the denoising process. When ${\displaystyle \lambda =0}$, there is no smoothing and the result is the same as minimizing the sum of squares. As ${\displaystyle \lambda \to \infty }$, however, the total variation term plays an increasingly strong role, which forces the result to have smaller total variation, at the expense of being less like the input (noisy) signal. Thus, the choice of regularization parameter is critical to achieving just the right amount of noise removal.

2D signal images

We now consider 2D signals y, such as images. The total-variation norm proposed by the 1992 article is

${\displaystyle V(y)=\sum _{i,j}{\sqrt {|y_{i+1,j}-y_{i,j}|^{2}+|y_{i,j+1}-y_{i,j}|^{2}}}}$

and is isotropic and not differentiable. A variation that is sometimes used, since it may sometimes be easier to minimize, is an anisotropic version

${\displaystyle V_{\operatorname {aniso} }(y)=\sum _{i,j}{\sqrt {|y_{i+1,j}-y_{i,j}|^{2}}}+{\sqrt {|y_{i,j+1}-y_{i,j}|^{2}}}=\sum _{i,j}|y_{i+1,j}-y_{i,j}|+|y_{i,j+1}-y_{i,j}|.}$

The standard total-variation denoising problem is still of the form

${\displaystyle \min _{y}[\operatorname {E} (x,y)+\lambda V(y)],}$

where E is the 2D L₂ norm. In contrast to the 1D case, solving this denoising is non-trivial. A recent algorithm that solves this is known as the primal dual method.^[4]

Due in part to much research in compressed sensing in the mid-2000s, there are many algorithms, such as the split-Bregman method, that solve variants of this problem.

Rudin–Osher–Fatemi PDE

Suppose that we are given a noisy image ${\displaystyle f}$ and wish to compute a denoised image ${\displaystyle u}$ over a 2D space. ROF showed that the minimization problem we are looking to solve is:^[5]

${\displaystyle \min _{u\in \operatorname {BV} (\Omega )}\;\|u\|_{\operatorname {TV} (\Omega )}+{\lambda \over 2}\int _{\Omega }(f-u)^{2}\,dx}$

where ${\textstyle \operatorname {BV} (\Omega )}$ is the set of functions with bounded variation over the domain ${\displaystyle \Omega }$, ${\textstyle \operatorname {TV} (\Omega )}$ is the total variation over the domain, and ${\textstyle \lambda }$ is a penalty term. When ${\textstyle u}$ is smooth, the total variation is equivalent to the integral of the gradient magnitude:

${\displaystyle \|u\|_{\operatorname {TV} (\Omega )}=\int _{\Omega }\|\nabla u\|\,dx}$

where ${\textstyle \|\cdot \|}$ is the Euclidean norm. Then the objective function of the minimization problem becomes:$${\displaystyle \min _{u\in \operatorname {BV} (\Omega )}\;\int _{\Omega }\left[\|\nabla u\|+{\lambda \over 2}(f-u)^{2}\right]\,dx}$$From this functional, the Euler-Lagrange equation for minimization – assuming no time-dependence – gives us the nonlinear elliptic partial differential equation:

${\displaystyle {\begin{cases}\nabla \cdot \left({\nabla u \over {\|\nabla u\|}}\right)+\lambda (f-u)=0,\quad &u\in \Omega \\{\partial u \over {\partial n}}=0,\quad &u\in \partial \Omega \end{cases}}}$

For some numerical algorithms, it is preferable to instead solve the time-dependent version of the ROF equation:$${\displaystyle {\partial u \over {\partial t}}=\nabla \cdot \left({\nabla u \over {\|\nabla u\|}}\right)+\lambda (f-u)}$$

Applications

The Rudin–Osher–Fatemi model was a pivotal component in producing the first image of a black hole.^[6]

See also

• Anisotropic diffusion
• Bounded variation
• Basis pursuit denoising
• Chambolle-Pock algorithm
• Digital image processing
• Lasso (statistics)
• Noise reduction
• Non-local means
• Signal processing
• Total variation

References

1. Rudin, L. I.; Osher, S.; Fatemi, E. (1992). "Nonlinear total variation based noise removal algorithms". Physica D. 60 (1–4): 259–268. Bibcode:1992PhyD...60..259R. CiteSeerX 10.1.1.117.1675. doi:10.1016/0167-2789(92)90242-f.
2. Strong, D.; Chan, T. (2003). "Edge-preserving and scale-dependent properties of total variation regularization". Inverse Problems. 19 (6): S165 – S187. Bibcode:2003InvPr..19S.165S. doi:10.1088/0266-5611/19/6/059. S2CID 250761777.
3. Little, M. A.; Jones, Nick S. (2010). "Sparse Bayesian Step-Filtering for High-Throughput Analysis of Molecular Machine Dynamics" (PDF). ICASSP 2010 Proceedings. 2010 IEEE International Conference on Acoustics, Speech and Signal Processing.
4. Chambolle, A. (2004). "An algorithm for total variation minimization and applications". Journal of Mathematical Imaging and Vision. 20 (1–2): 89–97. Bibcode:2004JMIV...20...89C. CiteSeerX 10.1.1.160.5226. doi:10.1023/B:JMIV.0000011325.36760.1e. S2CID 207622122.
5. Getreuer, Pascal (2012). "Rudin–Osher–Fatemi Total Variation Denoising using Split Bregman" (PDF).
6. "Rudin–Osher–Fatemi Model Captures Infinity and Beyond". IPAM. 2019-04-15. Retrieved 2019-08-04.

External links

• TVDIP: Full-featured Matlab 1D total variation denoising implementation.
• Efficient Primal-Dual Total Variation
• TV-L1 image denoising algorithm in Matlab