Computation times

137:06.173 total execution time for 80 files from all galleries:

Example	Time	Mem (MB)
Learned Primal-Dual algorithm for CT scan. (../../examples/unfolded/demo_learned_primal_dual.py)	84:06.674	0.0
Self-supervised learning with Equivariant Imaging for MRI. (../../examples/self-supervised-learning/demo_equivariant_imaging.py)	14:11.062	0.0
Fitting NIQE on a custom dataset (../../examples/metrics/demo_custom_niqe.py)	04:05.351	0.0
Deep Equilibrium (DEQ) algorithms for image deblurring (../../examples/unfolded/demo_DEQ.py)	02:39.076	0.0
Self-supervised learning from incomplete measurements of multiple operators. (../../examples/self-supervised-learning/demo_multioperator_imaging.py)	02:20.271	0.0
Benchmarking pretrained denoisers (../../examples/models/demo_denoiser_tour.py)	02:19.931	0.0
Self-supervised learning with Equivariant Splitting (../../examples/self-supervised-learning/demo_equivariant_splitting.py)	01:57.177	0.0
Scan-specific zero-shot SSDU for MRI (../../examples/self-supervised-learning/demo_scan_specific.py)	01:50.113	0.0
Reducing the memory and computational complexity of unfolded network training (../../examples/unfolded/demo_unfolded_constant_memory.py)	01:36.917	0.0
Inverse scattering problem (../../examples/physics/demo_scattering.py)	01:31.510	0.0
Use iterative reconstruction algorithms (../../examples/basics/demo_custom_optim.py)	01:27.344	0.0
Building your diffusion posterior sampling method using SDEs (../../examples/sampling/demo_diffusion_sde.py)	01:25.108	0.0
Learned Iterative Soft-Thresholding Algorithm (LISTA) for compressed sensing (../../examples/unfolded/demo_LISTA.py)	01:09.664	0.0
Learned iterative custom prior (../../examples/unfolded/demo_custom_prior_unfolded.py)	00:51.652	0.0
Blind deblurring with kernel estimation network (../../examples/blind-inverse-problems/demo_blind_deblurring.py)	00:51.075	0.0
Unfolded Chambolle-Pock for constrained image inpainting (../../examples/unfolded/demo_unfolded_constrained_LISTA.py)	00:50.748	0.0
Using state-of-the-art diffusion models from HuggingFace Diffusers with DeepInverse (../../examples/sampling/demo_diffusers.py)	00:45.352	0.0
Vanilla Unfolded algorithm for super-resolution (../../examples/unfolded/demo_vanilla_unfolded.py)	00:42.797	0.0
Distributed Denoiser with Image Tiling (../../examples/distributed/demo_denoiser_distributed.py)	00:42.507	0.0
Distributed Physics Operators (../../examples/distributed/demo_physics_distributed.py)	00:40.622	0.0
Self-supervised learning with measurement splitting (../../examples/self-supervised-learning/demo_splitting_loss.py)	00:35.267	0.0
DEAL denoising and reconstruction (../../examples/unfolded/demo_deal.py)	00:33.137	0.0
Inference and fine-tune a foundation model (../../examples/models/demo_foundation_model.py)	00:32.003	0.0
Low-field MRI denoising without ground truth (../../examples/self-supervised-learning/demo_lowfieldmri.py)	00:27.312	0.0
Distributed Plug-and-Play (PnP) Reconstruction (../../examples/distributed/demo_pnp_distributed.py)	00:26.118	0.0
Tour of MRI functionality in DeepInverse (../../examples/physics/demo_mri_tour.py)	00:25.864	0.0
Radio interferometric imaging with deepinverse (../../examples/external-libraries/demo_ri_basic.py)	00:25.831	0.0
Flow-Matching for posterior sampling and unconditional generation (../../examples/sampling/demo_flow_matching.py)	00:25.776	0.0
Self-supervised denoising with the UNSURE loss. (../../examples/self-supervised-learning/demo_unsure.py)	00:23.306	0.0
Imaging inverse problems with adversarial networks (../../examples/adversarial-learning/demo_gan_imaging.py)	00:22.628	0.0
Uncertainty quantification with PnP-ULA. (../../examples/sampling/demo_sampling.py)	00:21.128	0.0
Single-pixel imaging with Spyrit (../../examples/external-libraries/demo_connect_spyrit.py)	00:18.107	0.0
Blind denoising with noise level estimation (../../examples/blind-inverse-problems/demo_blind_denoising.py)	00:15.748	0.0
Image transforms for equivariance & augmentations (../../examples/transforms-equivariance/demo_transforms.py)	00:15.608	0.0
Self-supervised MRI reconstruction with Artifact2Artifact (../../examples/self-supervised-learning/demo_artifact2artifact.py)	00:14.765	0.0
Image transformations for Equivariant Imaging (../../examples/self-supervised-learning/demo_ei_transforms.py)	00:14.590	0.0
Image reconstruction with a diffusion model (../../examples/sampling/demo_ddrm.py)	00:13.881	0.0
Low-dose CT with ASTRA backend and Total-Variation (TV) prior (../../examples/external-libraries/demo_astra_tomography.py)	00:13.521	0.0
Spatial unwrapping and modulo imaging (../../examples/physics/demo_spatial_unwrapping.py)	00:13.439	0.0
Image deblurring with Total-Variation (TV) prior (../../examples/optimization/demo_TV_minimisation.py)	00:13.381	0.0
Using HuggingFace datasets (../../examples/external-libraries/demo_hf_dataset.py)	00:12.874	0.0
5 minute quickstart tutorial (../../examples/basics/demo_quickstart.py)	00:12.408	0.0
3D diffraction PSF (../../examples/physics/demo_microscopy_3d.py)	00:12.192	0.0
Regularization by Denoising (RED) for Super-Resolution. (../../examples/plug-and-play/demo_RED_GSPnP_SR.py)	00:10.883	0.0
Positron emission tomography (PET) in 3D (../../examples/physics/demo_pet3d.py)	00:10.599	0.0
Training a reconstruction model (../../examples/models/demo_training.py)	00:10.410	0.0
DPS – Posterior Sampling with Diffusion Models (../../examples/sampling/demo_dps.py)	00:09.810	0.0
3D denoising (../../examples/optimization/demo_3D_denoising.py)	00:09.603	0.0
Random phase retrieval and reconstruction methods. (../../examples/physics/demo_phase_retrieval.py)	00:09.453	0.0
Super-resolution with SRResNet (../../examples/models/demo_super_resolution.py)	00:09.315	0.0
Implementing DiffPIR (../../examples/sampling/demo_diffpir.py)	00:09.238	0.0
Patch priors for limited-angle computed tomography (../../examples/optimization/demo_patch_priors_CT.py)	00:08.291	0.0
Self-supervised denoising with the Neighbor2Neighbor loss. (../../examples/self-supervised-learning/demo_n2n_denoising.py)	00:07.217	0.0
Self-supervised denoising with the SURE loss. (../../examples/self-supervised-learning/demo_sure_denoising.py)	00:07.032	0.0
DPIR method for PnP image deblurring. (../../examples/plug-and-play/demo_PnP_DPIR_deblur.py)	00:06.976	0.0
Calibrating physics operators (../../examples/blind-inverse-problems/demo_optimizing_physics_parameter.py)	00:06.545	0.0
Poisson Inverse Problems with Maximum-Likelihood Expectation-Maximization (MLEM) (../../examples/optimization/demo_poisson_mlem.py)	00:06.312	0.0
Poisson denoising using Poisson2Sparse (../../examples/self-supervised-learning/demo_poisson2sparse.py)	00:06.079	0.0
Loading scientific images (../../examples/external-libraries/demo_io.py)	00:05.590	0.0
Tour of blur operators (../../examples/physics/demo_blur_tour.py)	00:05.040	0.0
Image inpainting with wavelet prior (../../examples/optimization/demo_wavelet_prior.py)	00:04.530	0.0
Image deblurring with custom deep explicit prior. (../../examples/optimization/demo_custom_prior.py)	00:04.478	0.0
Bring your own dataset (../../examples/basics/demo_custom_dataset.py)	00:04.381	0.0
Expected Patch Log Likelihood (EPLL) for Denoising and Inpainting (../../examples/optimization/demo_epll.py)	00:03.820	0.0
Tour of forward sensing operators (../../examples/physics/demo_physics_tour.py)	00:03.615	0.0
Positron emission tomography (PET) in 2D (../../examples/physics/demo_pet2d.py)	00:03.481	0.0
Remote sensing with satellite images (../../examples/physics/demo_remote_sensing.py)	00:03.372	0.0
Use a pretrained model (../../examples/basics/demo_pretrained_model.py)	00:03.182	0.0
PnP with custom optimization algorithm (Primal-Dual Condat-Vu) (../../examples/plug-and-play/demo_PnP_custom_optim.py)	00:03.107	0.0
Vanilla PnP for computed tomography (CT). (../../examples/plug-and-play/demo_vanilla_PnP.py)	00:03.023	0.0
Self-supervised denoising with the Generalized R2R loss. (../../examples/self-supervised-learning/demo_r2r_denoising.py)	00:02.926	0.0
Plug-and-Play algorithm with Mirror Descent for Poisson noise inverse problems. (../../examples/plug-and-play/demo_PnP_mirror_descent.py)	00:02.792	0.0
Low-intensity STED fluorescence microscopy denoising (../../examples/external-libraries/demo_microscopy_denoising.py)	00:02.439	0.0
Poisson-Gaussian Denoising with the Generalized Anscombe Transform (../../examples/physics/demo_anscombe.py)	00:02.153	0.0
Pattern Ordering in a Compressive Single Pixel Camera (../../examples/physics/demo_spc.py)	00:01.902	0.0
Ptychography phase retrieval (../../examples/physics/demo_ptychography.py)	00:01.409	0.0
Reconstructing an image using the deep image prior. (../../examples/optimization/demo_dip.py)	00:01.240	0.0
Single photon lidar operator for depth ranging. (../../examples/physics/demo_lidar.py)	00:00.785	0.0
Bring your own physics (../../examples/basics/demo_custom_physics.py)	00:00.656	0.0
Building your custom MCMC sampling algorithm. (../../examples/sampling/demo_custom_kernel.py)	00:00.651	0.0