deepmr.prox.LLRDenoiser#
- class deepmr.prox.LLRDenoiser(*args: Any, **kwargs: Any)[source]#
Local Low Rank denoising.
The solution is available in closed-form, thus the denoiser is cheap to compute.
- trainable#
If
True
, threshold value is trainable, otherwise it is not. The default isFalse
.- 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)[source]#
Methods
__init__
(ndim, W[, ths, trainable, S, ...])forward
(x)