deepmr.prox.LLRDenoiser

class deepmr.prox.LLRDenoiser(*args: Any, **kwargs: Any)

Local Low Rank denoising.

The solution is available in closed-form, thus the denoiser is cheap to compute.

ndim

Number of spatial dimensions.

Type:

int,

W

Patch size (assume isotropic).

Type:

int, optional

ths

Denoise threshold. The default is 0.1.

Type:

float, optional

trainable

If True, threshold value is trainable, otherwise it is not. The default is False.

Type:

bool, optional

S

Patch stride (assume isotropic). If not provided, use non-overlapping patches.

Type:

int, optional

rand_shift

If True, randomly shift across spatial dimensions before denoising.

Type:

bool, optional

axis

Axis assumed as coefficient axis (e.g., coils or contrasts). If not provided, use first axis to the left of spatial dimensions.

Type:

bool, optional

device

Device on which the wavelet transform is computed. The default is None (infer from input).

Type:

str, optional

__init__(ndim, W, ths=0.1, trainable=False, S=None, rand_shift=True, axis=None, device=None)

Methods

__init__(ndim, W[, ths, trainable, S, ...])
forward(x)