pyproximal.TV

class pyproximal.TV(dims: Tuple[int, ...] | None, sigma: float = 1.0, niter: int | Callable[[int], int] = 10, rtol: float = 0.0001, **kwargs: Any)

TV Norm proximal operator.

Proximal operator for the TV norm defined as: \(f(\mathbf{x}) = \sigma ||\mathbf{x}||_{\text{TV}}\).

Parameters:

dimstuple, optional

Number of samples for each dimension (None if only one dimension is available)

sigmafloat, optional

Multiplicative coefficient of TV norm

niterint or func, optional

Number of iterations of iterative scheme used to compute the proximal. This can be a constant number or a function that is called passing a counter which keeps track of how many times the prox method has been invoked before and returns the niter to be used.

rtolfloat, optional

Relative tolerance for stopping criterion.

Notes

The proximal algorithm is implemented following [1].

[1]

Beck, A. and Teboulle, M., “Fast gradient-based algorithms for constrained total variation image denoising and deblurring problems”, 2009.

Methods

__init__(dims[, sigma, niter, rtol])
affine_addition(v)	Affine addition
chain(g)	Chain
grad(x)	Gradient of the Moreau envelope of the function.
postcomposition(sigma)	Postcomposition
precomposition(a, b)	Precomposition
prox(*args, **kwargs)
proxdual(**kwargs)

Examples using pyproximal.TV

Norms

Norms

Denoising

Denoising