Computation times

170:03.178 total execution time for 82 files from all galleries:

Example	Time	Mem (MB)
Learned Primal-Dual algorithm for CT scan. (../../examples/unfolded/demo_learned_primal_dual.py)	88:25.855	0.0
Reducing the memory and computational complexity of unfolded network training (../../examples/unfolded/demo_unfolded_constant_memory.py)	28:49.139	0.0
Self-supervised learning with Equivariant Imaging for MRI. (../../examples/self-supervised-learning/demo_equivariant_imaging.py)	13:44.536	0.0
Fitting NIQE on a custom dataset (../../examples/metrics/demo_custom_niqe.py)	04:13.915	0.0
Deep Equilibrium (DEQ) algorithms for image deblurring (../../examples/unfolded/demo_DEQ.py)	03:06.908	0.0
Self-supervised learning from incomplete measurements of multiple operators. (../../examples/self-supervised-learning/demo_multioperator_imaging.py)	02:23.109	0.0
Self-supervised learning with Equivariant Splitting (../../examples/self-supervised-learning/demo_equivariant_splitting.py)	01:58.783	0.0
Scan-specific zero-shot SSDU for MRI (../../examples/self-supervised-learning/demo_scan_specific.py)	01:53.075	0.0
Benchmarking pretrained denoisers (../../examples/models/demo_denoiser_tour.py)	01:46.958	0.0
Inverse scattering problem (../../examples/physics/demo_scattering.py)	01:35.019	0.0
Vanilla Unfolded algorithm for super-resolution (../../examples/unfolded/demo_vanilla_unfolded.py)	01:34.422	0.0
Use iterative reconstruction algorithms (../../examples/basics/demo_custom_optim.py)	01:28.070	0.0
Building your diffusion posterior sampling method using SDEs (../../examples/sampling/demo_diffusion_sde.py)	01:26.289	0.0
Learned Iterative Soft-Thresholding Algorithm (LISTA) for compressed sensing (../../examples/unfolded/demo_LISTA.py)	01:12.329	0.0
Learned iterative custom prior (../../examples/unfolded/demo_custom_prior_unfolded.py)	01:06.150	0.0
Unfolded Chambolle-Pock for constrained image inpainting (../../examples/unfolded/demo_unfolded_constrained_LISTA.py)	01:04.950	0.0
Blind deblurring with kernel estimation network (../../examples/blind-inverse-problems/demo_blind_deblurring.py)	00:51.205	0.0
DEAL denoising and reconstruction (../../examples/unfolded/demo_deal.py)	00:44.836	0.0
Distributed Denoiser with Image Tiling (../../examples/distributed/demo_denoiser_distributed.py)	00:42.800	0.0
Distributed Physics Operators (../../examples/distributed/demo_physics_distributed.py)	00:40.495	0.0
Self-supervised learning with measurement splitting (../../examples/self-supervised-learning/demo_splitting_loss.py)	00:35.704	0.0
Inference and fine-tune a foundation model (../../examples/models/demo_foundation_model.py)	00:34.170	0.0
Using state-of-the-art diffusion models from HuggingFace Diffusers with DeepInverse (../../examples/sampling/demo_diffusers.py)	00:33.033	0.0
Self-supervised MRI reconstruction with Artifact2Artifact (../../examples/self-supervised-learning/demo_artifact2artifact.py)	00:31.564	0.0
Flow-Matching for posterior sampling and unconditional generation (../../examples/sampling/demo_flow_matching.py)	00:31.143	0.0
Radio interferometric imaging with deepinverse (../../examples/external-libraries/demo_ri_basic.py)	00:28.700	0.0
Low-field MRI denoising without ground truth (../../examples/self-supervised-learning/demo_lowfieldmri.py)	00:28.179	0.0
Distributed Plug-and-Play (PnP) Reconstruction (../../examples/distributed/demo_pnp_distributed.py)	00:26.221	0.0
Self-supervised denoising with the UNSURE loss. (../../examples/self-supervised-learning/demo_unsure.py)	00:23.185	0.0
Imaging inverse problems with adversarial networks (../../examples/adversarial-learning/demo_gan_imaging.py)	00:23.063	0.0
Uncertainty quantification with PnP-ULA. (../../examples/sampling/demo_sampling.py)	00:21.641	0.0
Blind denoising with noise level estimation (../../examples/blind-inverse-problems/demo_blind_denoising.py)	00:17.885	0.0
Single-pixel imaging with Spyrit (../../examples/external-libraries/demo_connect_spyrit.py)	00:17.810	0.0
Tour of MRI functionality in DeepInverse (../../examples/physics/demo_mri_tour.py)	00:17.498	0.0
Image transforms for equivariance & augmentations (../../examples/transforms-equivariance/demo_transforms.py)	00:16.721	0.0
Image transformations for Equivariant Imaging (../../examples/self-supervised-learning/demo_ei_transforms.py)	00:15.804	0.0
Image reconstruction with a diffusion model (../../examples/sampling/demo_ddrm.py)	00:14.142	0.0
Low-dose CT with ASTRA backend and Total-Variation (TV) prior (../../examples/external-libraries/demo_astra_tomography.py)	00:13.782	0.0
Image deblurring with Total-Variation (TV) prior (../../examples/optimization/demo_TV_minimisation.py)	00:13.448	0.0
5 minute quickstart tutorial (../../examples/basics/demo_quickstart.py)	00:11.369	0.0
Training a reconstruction model (../../examples/models/demo_training.py)	00:10.904	0.0
Random phase retrieval and reconstruction methods. (../../examples/physics/demo_phase_retrieval.py)	00:10.058	0.0
Regularization by Denoising (RED) for Super-Resolution. (../../examples/plug-and-play/demo_RED_GSPnP_SR.py)	00:09.929	0.0
DPS – Posterior Sampling with Diffusion Models (../../examples/sampling/demo_dps.py)	00:09.902	0.0
3D denoising (../../examples/optimization/demo_3D_denoising.py)	00:09.868	0.0
Implementing DiffPIR (../../examples/sampling/demo_diffpir.py)	00:09.547	0.0
Super-resolution with SRResNet (../../examples/models/demo_super_resolution.py)	00:09.446	0.0
Positron emission tomography (PET) in 3D (../../examples/physics/demo_pet3d.py)	00:08.916	0.0
Patch priors for limited-angle computed tomography (../../examples/optimization/demo_patch_priors_CT.py)	00:08.467	0.0
Using HuggingFace datasets (../../examples/external-libraries/demo_hf_dataset.py)	00:08.260	0.0
Self-supervised denoising with the SURE loss. (../../examples/self-supervised-learning/demo_sure_denoising.py)	00:07.920	0.0
Spatial unwrapping and modulo imaging (../../examples/physics/demo_spatial_unwrapping.py)	00:07.558	0.0
Self-supervised denoising with the Neighbor2Neighbor loss. (../../examples/self-supervised-learning/demo_n2n_denoising.py)	00:07.249	0.0
Poisson denoising using Poisson2Sparse (../../examples/self-supervised-learning/demo_poisson2sparse.py)	00:07.246	0.0
Multi-scale Plug-and-Play for Inpainting (../../examples/plug-and-play/demo_PnP_multiscale.py)	00:07.000	0.0
DPIR method for PnP image deblurring. (../../examples/plug-and-play/demo_PnP_DPIR_deblur.py)	00:06.851	0.0
Calibrating physics operators (../../examples/blind-inverse-problems/demo_optimizing_physics_parameter.py)	00:06.607	0.0
3D diffraction PSF (../../examples/physics/demo_microscopy_3d.py)	00:05.878	0.0
Tour of blur operators (../../examples/physics/demo_blur_tour.py)	00:05.050	0.0
Image inpainting with wavelet prior (../../examples/optimization/demo_wavelet_prior.py)	00:04.902	0.0
Image deblurring with custom deep explicit prior. (../../examples/optimization/demo_custom_prior.py)	00:04.381	0.0
Bring your own dataset (../../examples/basics/demo_custom_dataset.py)	00:04.334	0.0
Poisson Inverse Problems with Maximum-Likelihood Expectation-Maximization (MLEM) (../../examples/optimization/demo_poisson_mlem.py)	00:03.984	0.0
Expected Patch Log Likelihood (EPLL) for Denoising and Inpainting (../../examples/optimization/demo_epll.py)	00:03.850	0.0
Tour of forward sensing operators (../../examples/physics/demo_physics_tour.py)	00:03.645	0.0
Positron emission tomography (PET) in 2D (../../examples/physics/demo_pet2d.py)	00:03.579	0.0
Remote sensing with satellite images (../../examples/physics/demo_remote_sensing.py)	00:03.437	0.0
Vanilla PnP for computed tomography (CT). (../../examples/plug-and-play/demo_vanilla_PnP.py)	00:03.271	0.0
Use a pretrained model (../../examples/basics/demo_pretrained_model.py)	00:03.254	0.0
PnP with custom optimization algorithm (Primal-Dual Condat-Vu) (../../examples/plug-and-play/demo_PnP_custom_optim.py)	00:03.120	0.0
Self-supervised denoising with the Generalized R2R loss. (../../examples/self-supervised-learning/demo_r2r_denoising.py)	00:02.920	0.0
Plug-and-Play algorithm with Mirror Descent for Poisson noise inverse problems. (../../examples/plug-and-play/demo_PnP_mirror_descent.py)	00:02.813	0.0
Loading scientific images (../../examples/external-libraries/demo_io.py)	00:02.790	0.0
Poisson-Gaussian Denoising with the Generalized Anscombe Transform (../../examples/physics/demo_anscombe.py)	00:02.237	0.0
Pattern Ordering in a Compressive Single Pixel Camera (../../examples/physics/demo_spc.py)	00:02.110	0.0
Low-intensity STED fluorescence microscopy denoising (../../examples/external-libraries/demo_microscopy_denoising.py)	00:02.048	0.0
Ptychography phase retrieval (../../examples/physics/demo_ptychography.py)	00:01.465	0.0
Reconstructing an image using the deep image prior. (../../examples/optimization/demo_dip.py)	00:01.276	0.0
Spectral Methods for Non-Circular Deblurring with Liu-Jia Padding (../../examples/physics/demo_liu_jia_padding.py)	00:00.991	0.0
Single photon lidar operator for depth ranging. (../../examples/physics/demo_lidar.py)	00:00.792	0.0
Bring your own physics (../../examples/basics/demo_custom_physics.py)	00:00.765	0.0
Building your custom MCMC sampling algorithm. (../../examples/sampling/demo_custom_kernel.py)	00:00.649	0.0