Proximal Operators

Sub-package containing regularization routines.

DeepMR provides regularization routines for plug-n-play compressed sensing.

Classical Denoisers

Classical (i.e., non-trainable) denoisers can be used inside iterative reconstructions as regularizars (aka, PnP reconstruction).

DeepMR expose denoisers both as torch.nn objects (to be chained in DL-based reconstructions) and functional forms (e.g., for simple standalone denoising).

Currently available denoisers are Wavelet (WaveletDenoiser, WaveletDictDenoiser), Local Low Rank (LLRDenoiser) and Total (Generalized) Variation (TVDenoiser, ``TGVDenoiser`).

Both WaveletDenoiser / WaveletDictDenoiser and TVDenoiser / TGVDenoiser are adapted for complex-value inputs from the corresponding DeepInverse implementations.

deepmr.prox.WaveletDenoiser	Orthogonal Wavelet denoising with the \(\ell_1\) norm.
deepmr.prox.WaveletDictDenoiser	Overcomplete Wavelet denoising with the \(\ell_1\) norm.
deepmr.prox.TVDenoiser	Proximal operator of the isotropic Total Variation operator.
deepmr.prox.TGVDenoiser	Proximal operator of (2nd order) Total Generalised Variation operator.
deepmr.prox.LLRDenoiser	Local Low Rank denoising.
deepmr.prox.wavelet_dict_denoise	Apply overcomplete Wavelet denoising with the \(\ell_1\) norm.
deepmr.prox.wavelet_denoise	Apply orthogonal Wavelet denoising with the \(\ell_1\) norm.
deepmr.prox.tv_denoise	Apply isotropic Total Variation denoising.
deepmr.prox.tgv_denoise	Apply Total Generalized Variation denoising.
deepmr.prox.llr_denoise	Apply Local Low Rank denoising.