DIPA: Distilled Preconditioned Algorithms for Solving Imaging Inverse Problems | Paper Citation Record

Observation aca967f1-2950-4f30-ba77-7065b2bb2b56 · outbound

This paper cites Preconditioning strategies for nonlinear conjugate gradient methods, based on quasi-newton updates, in: AIP conference proceedings, AIP Publishing.

DIPA: Distilled Preconditioned Algorithms for Solving Imaging Inverse Problems Preconditioning strategies for nonlinear conjugate gradient methods, based on quasi-newton updates, in: AIP conference proceedings, AIP Publishing

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T14:27:24.471942Z

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Observation 09a9edd1-4a00-41f9-893c-2f159c1608ce · outbound

This paper cites Adaptive precision in block-jacobi preconditioning for iterative sparse linear system solvers.

DIPA: Distilled Preconditioned Algorithms for Solving Imaging Inverse Problems Adaptive precision in block-jacobi preconditioning for iterative sparse linear system solvers

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T14:27:24.464012Z

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Observation 302237d9-2a43-4fd3-9442-1d014f799886 · outbound

This paper cites Contour detection and hierarchical image segmentation.

DIPA: Distilled Preconditioned Algorithms for Solving Imaging Inverse Problems Contour detection and hierarchical image segmentation

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T14:27:24.467881Z

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Observation dff2054d-bd17-4e84-83d8-c6c966db020b · outbound

This paper cites Deep learning methods for solving linear inverse problems: Research directions and paradigms.

DIPA: Distilled Preconditioned Algorithms for Solving Imaging Inverse Problems Deep learning methods for solving linear inverse problems: Research directions and paradigms

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T14:27:24.458337Z

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Observation 9a696c8d-8ac5-4266-9741-87785f271b2b · outbound

This paper cites A fast iterative shrinkage-thresholding algorithm for linear inverse problems.

DIPA: Distilled Preconditioned Algorithms for Solving Imaging Inverse Problems A fast iterative shrinkage-thresholding algorithm for linear inverse problems

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T14:27:24.454499Z

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Observation ce2aa0f9-d604-4196-82cc-8eb3689eb545 · outbound

This paper cites Introduction to inverse problems in imaging.

DIPA: Distilled Preconditioned Algorithms for Solving Imaging Inverse Problems Introduction to inverse problems in imaging

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T14:27:24.446594Z

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Observation 39a9e367-2c1f-4ccf-95df-4c7bd78a42d4 · outbound

This paper cites Linear inverse problems with discrete data.

DIPA: Distilled Preconditioned Algorithms for Solving Imaging Inverse Problems Linear inverse problems with discrete data

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T14:27:24.477969Z

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Observation a806b163-63e5-4922-943c-4398a8f5a6a4 · outbound

This paper cites Distributed optimization and statistical learning via the alternating direction method of multipliers.

DIPA: Distilled Preconditioned Algorithms for Solving Imaging Inverse Problems Distributed optimization and statistical learning via the alternating direction method of multipliers

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T14:27:24.416182Z

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Observation eb3f6b9f-f58f-4c00-835c-74450952461e · outbound

This paper cites Non-Local Means Denoising.

DIPA: Distilled Preconditioned Algorithms for Solving Imaging Inverse Problems Non-Local Means Denoising

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T14:27:24.479780Z

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Observation ae75050f-a1dd-4983-9167-532802bc9629 · outbound

This paper cites Image denoising: Can plain neural networks compete with bm3d?, in: 2012 IEEE conference on computer vision and pattern recognition, IEEE.

DIPA: Distilled Preconditioned Algorithms for Solving Imaging Inverse Problems Image denoising: Can plain neural networks compete with bm3d?, in: 2012 IEEE conference on computer vision and pattern recognition, IEEE

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T14:27:24.418747Z

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Observation 34ca18da-a874-45d7-9e9c-dd7e29f0c2dc · outbound

This paper cites A developer’s guide to audit logging.

DIPA: Distilled Preconditioned Algorithms for Solving Imaging Inverse Problems A developer’s guide to audit logging

Reference 11

Resolution: malformed identifier; doi_truncated, observed 2026-05-19T14:27:23.798543Z

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Observation 258d4d6a-b8a3-4f82-b797-e0110acf762d · outbound

This paper cites Plug-and-play admm for image restoration: Fixed-point convergence and applications.

DIPA: Distilled Preconditioned Algorithms for Solving Imaging Inverse Problems Plug-and-play admm for image restoration: Fixed-point convergence and applications

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T14:27:24.411727Z

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Observation 495c2bad-f047-488d-8841-9ba593ef86fc · outbound

This paper cites Nonlinearly preconditioned krylov subspace methods for discrete newton algorithms.

DIPA: Distilled Preconditioned Algorithms for Solving Imaging Inverse Problems Nonlinearly preconditioned krylov subspace methods for discrete newton algorithms

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T14:27:24.408579Z

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Observation cf4bebf2-8bd2-4709-b3c8-eb2a413dd13e · outbound

This paper cites Learning efficient object detection models with knowledge distillation, in: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R.

DIPA: Distilled Preconditioned Algorithms for Solving Imaging Inverse Problems Learning efficient object detection models with knowledge distillation, in: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T14:27:24.432712Z

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Observation d9fea5d7-49df-4b9d-9843-7e356cff5848 · outbound

This paper cites Multiscale cholesky preconditioning for ill-conditioned problems.

DIPA: Distilled Preconditioned Algorithms for Solving Imaging Inverse Problems Multiscale cholesky preconditioning for ill-conditioned problems

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T14:27:24.373762Z

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Observation 32f50263-0509-41ab-bcb4-b898ee8e39c6 · outbound

This paper cites Imagedenoisingwithblock-matchingand3dfiltering,in:Imageprocessing:algorithms and systems, neural networks, and machine learning, SPIE.

DIPA: Distilled Preconditioned Algorithms for Solving Imaging Inverse Problems Imagedenoisingwithblock-matchingand3dfiltering,in:Imageprocessing:algorithms and systems, neural networks, and machine learning, SPIE

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T14:27:24.435067Z

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Observation f221a579-7b84-402b-884b-c08f70477d7c · outbound

This paper cites Multilevel preconditioning and adaptive sparse solution of inverse problems.

DIPA: Distilled Preconditioned Algorithms for Solving Imaging Inverse Problems Multilevel preconditioning and adaptive sparse solution of inverse problems

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T14:27:24.440799Z

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Observation b17d21e1-ec85-4c08-ab59-bb247f826bd5 · outbound

This paper cites A preconditioner for a primal-dual newton conjugate gradient method for compressed sensing problems.

DIPA: Distilled Preconditioned Algorithms for Solving Imaging Inverse Problems A preconditioner for a primal-dual newton conjugate gradient method for compressed sensing problems

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T14:27:24.380469Z

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Observation 273afaf1-cb59-4b65-b510-6167f617c654 · outbound

This paper cites An iterative thresholding algorithm for linear inverse problems with a sparsity constraint.

DIPA: Distilled Preconditioned Algorithms for Solving Imaging Inverse Problems An iterative thresholding algorithm for linear inverse problems with a sparsity constraint

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T14:27:24.450390Z

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Observation 80b32f8d-c5cf-452d-b67b-b85763801610 · outbound

This paper cites Inversion by Direct Iteration: An Alternative to Denoising Diffusion for Image Restoration.

DIPA: Distilled Preconditioned Algorithms for Solving Imaging Inverse Problems Inversion by Direct Iteration: An Alternative to Denoising Diffusion for Image Restoration

Reference 20

Resolution: verified exact; arxiv_id, observed 2026-05-19T14:27:24.094214Z

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Observation e25a4a53-01cd-4b92-a5d3-594973a75e44 · outbound

This paper cites The mnist database of handwritten digit images for machine learning research [best of the web].

DIPA: Distilled Preconditioned Algorithms for Solving Imaging Inverse Problems The mnist database of handwritten digit images for machine learning research [best of the web]

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T14:27:24.513382Z

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Observation 67aebde4-0cb5-4d40-8c94-c2b2832a3186 · outbound

This paper cites Numerical methods for unconstrained optimization and nonlinear equations.

DIPA: Distilled Preconditioned Algorithms for Solving Imaging Inverse Problems Numerical methods for unconstrained optimization and nonlinear equations

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T14:27:24.503603Z

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Observation 384eaa89-7f09-4c14-ae33-da45e154d0b3 · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

DIPA: Distilled Preconditioned Algorithms for Solving Imaging Inverse Problems An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 23

Resolution: verified exact; local_arxiv, observed 2026-05-19T14:27:24.083463Z

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Observation c439c6eb-a574-4afc-a3ea-529bba666e60 · outbound

This paper cites Single-pixel imaging via compressive sampling.

DIPA: Distilled Preconditioned Algorithms for Solving Imaging Inverse Problems Single-pixel imaging via compressive sampling

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T14:27:24.428151Z

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Observation ecc1e445-7dd8-4720-a421-0ebec5efb3c0 · outbound

This paper cites Greedy Learning to Optimize with Convergence Guarantees.

DIPA: Distilled Preconditioned Algorithms for Solving Imaging Inverse Problems Greedy Learning to Optimize with Convergence Guarantees

Reference 25

Resolution: verified exact; arxiv_id, observed 2026-07-08T01:18:17.138077Z

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Observation adc9a36c-0ec2-4c7e-8abe-0269ba0518ef · outbound

This paper cites Conjugate-gradient preconditioning methods for shift-variant pet image reconstruction.

DIPA: Distilled Preconditioned Algorithms for Solving Imaging Inverse Problems Conjugate-gradient preconditioning methods for shift-variant pet image reconstruction

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T14:27:24.423906Z

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Observation ea1b1a62-953d-49ce-aeb4-c389d92b0782 · outbound

This paper cites On the origins of linear and non-linear preconditioning, in: Domain decomposition methods in science and engineering XXIII, Springer.

DIPA: Distilled Preconditioned Algorithms for Solving Imaging Inverse Problems On the origins of linear and non-linear preconditioning, in: Domain decomposition methods in science and engineering XXIII, Springer

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T14:27:24.426168Z

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Observation ac2b58ae-7f33-4278-a6bf-528619a63152 · outbound

This paper cites Image restoration by denoising diffusion models with iteratively preconditioned guidance, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp.

DIPA: Distilled Preconditioned Algorithms for Solving Imaging Inverse Problems Image restoration by denoising diffusion models with iteratively preconditioned guidance, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T14:27:24.430389Z

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Observation 74304cc6-5470-4f7d-a55e-5905943ceee0 · outbound

This paper cites Domain transform for edge-aware image and video processing, in: ACM SIGGRAPH 2011 Papers, Association for Computing Machinery, New York, NY, USA.

DIPA: Distilled Preconditioned Algorithms for Solving Imaging Inverse Problems Domain transform for edge-aware image and video processing, in: ACM SIGGRAPH 2011 Papers, Association for Computing Machinery, New York, NY, USA

Reference 29

Resolution: verified exact; arxiv_id, observed 2026-05-19T14:27:23.827928Z

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Observation a6ebb12f-9e89-4381-bf3d-5d76e5b16380 · outbound

This paper cites Tikhonov regularization and total least squares.

DIPA: Distilled Preconditioned Algorithms for Solving Imaging Inverse Problems Tikhonov regularization and total least squares

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T14:27:24.489650Z

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Observation be98738b-6eb1-43c1-bbaf-17715b6864b5 · outbound

This paper cites Knowledge distillation: A survey.

DIPA: Distilled Preconditioned Algorithms for Solving Imaging Inverse Problems Knowledge distillation: A survey

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T14:27:24.505418Z

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Observation 7762a3c0-2f8b-4de4-ba72-39de1165670f · outbound

This paper cites GSNR: Graph Smooth Null-Space Representation for Inverse Problems.

DIPA: Distilled Preconditioned Algorithms for Solving Imaging Inverse Problems GSNR: Graph Smooth Null-Space Representation for Inverse Problems

Reference 32

Resolution: verified exact; local_arxiv, observed 2026-05-19T14:27:24.089595Z

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Observation b78c1d4c-5ceb-49f8-9450-9a963bc3f1fb · outbound

This paper cites an unresolved cited work.

DIPA: Distilled Preconditioned Algorithms for Solving Imaging Inverse Problems Unresolved cited work

Reference 33

Resolution: verified exact; arxiv_id, observed 2026-05-19T14:27:23.819934Z

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Observation 93862609-9b69-4d7c-953c-77005e5a846e · outbound

This paper cites Image restoration.

DIPA: Distilled Preconditioned Algorithms for Solving Imaging Inverse Problems Image restoration

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T14:27:24.421439Z

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Observation 899602b2-bcdd-4441-a94c-e7a4fc25b07d · outbound

This paper cites Half-quadratic-based iterative minimization for robust sparse representation.

DIPA: Distilled Preconditioned Algorithms for Solving Imaging Inverse Problems Half-quadratic-based iterative minimization for robust sparse representation

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T14:27:24.462187Z

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Observation d38f56d0-5d0c-446d-9776-674b1151c501 · outbound

This paper cites Distilling the Knowledge in a Neural Network.

DIPA: Distilled Preconditioned Algorithms for Solving Imaging Inverse Problems Distilling the Knowledge in a Neural Network

Reference 36

Resolution: verified exact; local_arxiv, observed 2026-05-19T14:27:24.069861Z

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Observation 230d68cf-d593-4827-868b-160a7f2873d4 · outbound

This paper cites Polynomial preconditioners for regularized linear inverse problems.

DIPA: Distilled Preconditioned Algorithms for Solving Imaging Inverse Problems Polynomial preconditioners for regularized linear inverse problems

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T14:27:24.401556Z

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Observation 601a5094-d3d8-4936-aa8f-572d737dd4fc · outbound

This paper cites NPN: Non-Linear Projections of the Null-Space for Imaging Inverse Problems.

DIPA: Distilled Preconditioned Algorithms for Solving Imaging Inverse Problems NPN: Non-Linear Projections of the Null-Space for Imaging Inverse Problems

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T14:27:24.377006Z

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Observation f4598fc1-3bc8-46dc-8b21-8caf61e2058d · outbound

This paper cites Sparsity regularization in inverse problems.

DIPA: Distilled Preconditioned Algorithms for Solving Imaging Inverse Problems Sparsity regularization in inverse problems

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T14:27:24.437014Z

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No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 386115ed-2033-446c-bce6-e1901c779389 · outbound

This paper cites Polynomial preconditioners for conjugate gradient calculations.

DIPA: Distilled Preconditioned Algorithms for Solving Imaging Inverse Problems Polynomial preconditioners for conjugate gradient calculations

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T14:27:24.481809Z

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Observation ec919eb9-a0a7-4b4b-9923-482d3d54bdb9 · outbound

This paper cites Plug-and-play methods for integrating physical and learned models in computational imaging: Theory, algorithms, and applications.

DIPA: Distilled Preconditioned Algorithms for Solving Imaging Inverse Problems Plug-and-play methods for integrating physical and learned models in computational imaging: Theory, algorithms, and applications

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T14:27:24.483729Z

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Observation e06fa90c-ecc2-43a7-bef5-1c04a07309b4 · outbound

This paper cites Adam: A Method for Stochastic Optimization.

DIPA: Distilled Preconditioned Algorithms for Solving Imaging Inverse Problems Adam: A Method for Stochastic Optimization

Reference 42

Resolution: verified exact; local_arxiv, observed 2026-05-19T14:27:24.080548Z

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Observation 5a43ffe7-8cca-4665-a012-443f9a6476a7 · outbound

This paper cites FastMRI:Apubliclyavailable rawk-space andDICOMdataset ofknee images foraccelerated MRimagereconstruction using machine learning.

DIPA: Distilled Preconditioned Algorithms for Solving Imaging Inverse Problems FastMRI:Apubliclyavailable rawk-space andDICOMdataset ofknee images foraccelerated MRimagereconstruction using machine learning

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T14:27:24.404947Z

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Observation 2df5c679-603b-48c0-941a-51809f095825 · outbound

This paper cites Gradient-based learning applied to document recognition.

DIPA: Distilled Preconditioned Algorithms for Solving Imaging Inverse Problems Gradient-based learning applied to document recognition

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T14:27:24.392659Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 2648589a-4aac-4e6e-bde2-417f8aa4a0f9 · outbound

This paper cites an unresolved cited work.

DIPA: Distilled Preconditioned Algorithms for Solving Imaging Inverse Problems Unresolved cited work

Reference 45

Resolution: unresolved; raw_fallback, observed 2026-05-19T14:27:24.414098Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation c9794e12-745b-41f3-ac18-bbf157547e4c · outbound

This paper cites A note on adaptive nonlinear preconditioning techniques.

DIPA: Distilled Preconditioned Algorithms for Solving Imaging Inverse Problems A note on adaptive nonlinear preconditioning techniques

Reference 46

Resolution: verified exact; doi, observed 2026-05-19T14:27:23.823113Z

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Observation 81b7fc6b-38f4-4c20-b3fb-dac35827f257 · outbound

This paper cites Structured knowledge distillation for semantic segmentation, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

DIPA: Distilled Preconditioned Algorithms for Solving Imaging Inverse Problems Structured knowledge distillation for semantic segmentation, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T14:27:24.495438Z

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No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 899bbd22-5bf3-4cdc-9983-bef347fcbcd8 · outbound

This paper cites Deep learning face attributes in the wild, in: Proceedings of International Conference on Computer Vision (ICCV).

DIPA: Distilled Preconditioned Algorithms for Solving Imaging Inverse Problems Deep learning face attributes in the wild, in: Proceedings of International Conference on Computer Vision (ICCV)

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T14:27:24.491676Z

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Observation 1a9166dd-f205-4fd6-a24f-9913c44671e8 · outbound

This paper cites A convnet for the 2020s, in: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp.

DIPA: Distilled Preconditioned Algorithms for Solving Imaging Inverse Problems A convnet for the 2020s, in: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T14:27:24.501373Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 7bbbdb3b-213b-4a61-aaf0-b3b1fc7c0b1b · outbound

This paper cites Decoupled Weight Decay Regularization.

DIPA: Distilled Preconditioned Algorithms for Solving Imaging Inverse Problems Decoupled Weight Decay Regularization

Reference 50

Resolution: verified exact; local_arxiv, observed 2026-05-19T14:27:24.073920Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 7f073500-29e1-4d72-8ace-77dc646b6545 · outbound

This paper cites Compressed sensing mri.

DIPA: Distilled Preconditioned Algorithms for Solving Imaging Inverse Problems Compressed sensing mri

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T14:27:24.497337Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 039675f4-0a76-4963-9853-13a8a6d04216 · outbound

This paper cites Improving medical image denoising via a lightweight plug-and-play module, in: 2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE.

DIPA: Distilled Preconditioned Algorithms for Solving Imaging Inverse Problems Improving medical image denoising via a lightweight plug-and-play module, in: 2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T14:27:24.465937Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation abfc4659-603a-4997-9e4c-aa18844a8ece · outbound

This paper cites Kd-mri: A knowledge distillation framework for image reconstruction and image restoration in mri workflow, in: Medical imaging with deep learning, PMLR.

DIPA: Distilled Preconditioned Algorithms for Solving Imaging Inverse Problems Kd-mri: A knowledge distillation framework for image reconstruction and image restoration in mri workflow, in: Medical imaging with deep learning, PMLR

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T14:27:24.469803Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 8c9da995-1d57-43d3-b828-78a5537a9420 · outbound

This paper cites Efficient preconditioners for optimality systems arising in connection with inverse problems.

DIPA: Distilled Preconditioned Algorithms for Solving Imaging Inverse Problems Efficient preconditioners for optimality systems arising in connection with inverse problems

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T14:27:24.476151Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 3feb3e5d-b047-4fa1-93d6-1d1a223fc699 · outbound

This paper cites Deep learning techniques for inverse problems in imaging.

DIPA: Distilled Preconditioned Algorithms for Solving Imaging Inverse Problems Deep learning techniques for inverse problems in imaging

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T14:27:24.398526Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 3cc71a2d-2e36-4127-8fd4-ed7188b9caca · outbound

This paper cites Proximal algorithms.

DIPA: Distilled Preconditioned Algorithms for Solving Imaging Inverse Problems Proximal algorithms

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T14:27:24.383755Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation de4d7b6a-3e3b-4b03-aa90-31f0c7094bef · outbound

This paper cites Preconditioners for krylov subspace methods: An overview.

DIPA: Distilled Preconditioned Algorithms for Solving Imaging Inverse Problems Preconditioners for krylov subspace methods: An overview

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T14:27:24.460148Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 3a5da1a7-2a14-40da-a3b4-2ccbad6d4432 · outbound

This paper cites Coil sensitivity encoding for fast mri, in: Proceedings of the ISMRM 6th Annual Meeting, Sydney.

DIPA: Distilled Preconditioned Algorithms for Solving Imaging Inverse Problems Coil sensitivity encoding for fast mri, in: Proceedings of the ISMRM 6th Annual Meeting, Sydney

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T14:27:24.442892Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation c3353fc8-49de-4c49-be48-2c2c3cc02017 · outbound

This paper cites On the expressive power of deep neural networks, in: international conference on machine learning, PMLR.

DIPA: Distilled Preconditioned Algorithms for Solving Imaging Inverse Problems On the expressive power of deep neural networks, in: international conference on machine learning, PMLR

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T14:27:24.444767Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 5e9893f0-c95b-4eda-b653-0b7f621c101f · outbound

This paper cites The little engine that could: Regularization by denoising (red).

DIPA: Distilled Preconditioned Algorithms for Solving Imaging Inverse Problems The little engine that could: Regularization by denoising (red)

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T14:27:24.448529Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 3e66b105-b92d-45f7-9909-2709c26610db · outbound

This paper cites an unresolved cited work.

DIPA: Distilled Preconditioned Algorithms for Solving Imaging Inverse Problems Unresolved cited work

Reference 61

Resolution: unresolved; raw_fallback, observed 2026-05-19T14:27:24.452440Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 0456f2db-48e8-432c-a517-895a593ee19e · outbound

This paper cites The perceptron: a probabilistic model for information storage and organization in the brain.

DIPA: Distilled Preconditioned Algorithms for Solving Imaging Inverse Problems The perceptron: a probabilistic model for information storage and organization in the brain

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T14:27:24.456410Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation ad1316bd-6dfa-4608-8f31-6e056f6f08ec · outbound

This paper cites End-to-end variational networks for accelerated mri reconstruction, in: MICCAI 2020, Springer.

DIPA: Distilled Preconditioned Algorithms for Solving Imaging Inverse Problems End-to-end variational networks for accelerated mri reconstruction, in: MICCAI 2020, Springer

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T14:27:24.473948Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 4043aa51-deb1-41cb-adc4-c87bdb24bf3a · outbound

This paper cites Edge-preserving and scale-dependent properties of total variation regularization.

DIPA: Distilled Preconditioned Algorithms for Solving Imaging Inverse Problems Edge-preserving and scale-dependent properties of total variation regularization

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T14:27:24.507347Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 518ef31a-81c9-46b1-be4c-ba92909aec4e · outbound

This paper cites DeepInverse: A deep learning framework for inverse problems in imaging.

DIPA: Distilled Preconditioned Algorithms for Solving Imaging Inverse Problems DeepInverse: A deep learning framework for inverse problems in imaging

Reference 65

Resolution: verified exact; doi, observed 2026-05-19T14:27:23.807937Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 377246b5-b8eb-46cc-aa58-0ad70b989c15 · outbound

This paper cites Provably convergent plug-and-play quasi-newton methods.

DIPA: Distilled Preconditioned Algorithms for Solving Imaging Inverse Problems Provably convergent plug-and-play quasi-newton methods

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T14:27:24.395579Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation d5b1a2d2-b8e4-47cb-b7a2-3806b185530c · outbound

This paper cites Image restoration and reconstruction using targeted plug-and-play priors.

DIPA: Distilled Preconditioned Algorithms for Solving Imaging Inverse Problems Image restoration and reconstruction using targeted plug-and-play priors

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T14:27:24.388723Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 34b69593-e761-4c20-816b-4974c62b954e · outbound

This paper cites A survey on super-resolution imaging.

DIPA: Distilled Preconditioned Algorithms for Solving Imaging Inverse Problems A survey on super-resolution imaging

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T14:27:24.438919Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 5877a31c-88e8-49ac-adbd-97fd71a54401 · outbound

This paper cites Plug-and-play priors for model based reconstruction, in: 2013 IEEE Global Conference on Signal and Information Processing, pp.

DIPA: Distilled Preconditioned Algorithms for Solving Imaging Inverse Problems Plug-and-play priors for model based reconstruction, in: 2013 IEEE Global Conference on Signal and Information Processing, pp

Reference 69

Resolution: verified exact; arxiv_id, observed 2026-05-19T14:27:23.814389Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation ae62d576-af6b-46a1-b2f1-9054a0226a60 · outbound

This paper cites Cbam: Convolutional block attention module, in: Proceedings of the European conference on computer vision (ECCV), pp.

DIPA: Distilled Preconditioned Algorithms for Solving Imaging Inverse Problems Cbam: Convolutional block attention module, in: Proceedings of the European conference on computer vision (ECCV), pp

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T14:27:24.499340Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation c4e080ea-8ec8-4e70-b95a-792f22a892cb · outbound

This paper cites Fista-net: Learning a fast iterative shrinkage thresholding network for inverse problems in imaging.

DIPA: Distilled Preconditioned Algorithms for Solving Imaging Inverse Problems Fista-net: Learning a fast iterative shrinkage thresholding network for inverse problems in imaging

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T14:27:24.509186Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation d4f0d4ab-7547-4c59-8135-d51a0b03c5b6 · outbound

This paper cites A multiscale and multidepth convolutional neural network for remote sensing imagery pan-sharpening.

DIPA: Distilled Preconditioned Algorithms for Solving Imaging Inverse Problems A multiscale and multidepth convolutional neural network for remote sensing imagery pan-sharpening

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T14:27:24.493504Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation ae11759b-4bd8-452c-a267-4b07d1db848c · outbound

This paper cites Paying More Attention to Attention: Improving the Performance of Convolutional Neural Networks via Attention Transfer.

DIPA: Distilled Preconditioned Algorithms for Solving Imaging Inverse Problems Paying More Attention to Attention: Improving the Performance of Convolutional Neural Networks via Attention Transfer

Reference 73

Resolution: verified exact; local_arxiv, observed 2026-05-19T14:27:24.086626Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation e2d53299-eb65-499b-b3ae-189ef51b4745 · outbound

This paper cites Restormer: Efficient transformer for high-resolution image restoration, in: IEEE/CVF CVPR, pp.

DIPA: Distilled Preconditioned Algorithms for Solving Imaging Inverse Problems Restormer: Efficient transformer for high-resolution image restoration, in: IEEE/CVF CVPR, pp

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T14:27:24.511289Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation f8abe46e-9657-4c07-add9-8a51e41b772f · outbound

This paper cites Learning nonlocal sparse and low-rank models for image compressive sensing: Nonlocal sparse and low-rank modeling.

DIPA: Distilled Preconditioned Algorithms for Solving Imaging Inverse Problems Learning nonlocal sparse and low-rank models for image compressive sensing: Nonlocal sparse and low-rank modeling

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T14:27:24.485916Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 73d814c8-a9b7-4dba-892c-2b43fddfab53 · outbound

This paper cites Plug-and-play image restoration with deep denoiser prior.

DIPA: Distilled Preconditioned Algorithms for Solving Imaging Inverse Problems Plug-and-play image restoration with deep denoiser prior

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T14:27:24.487865Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

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Outbound references

Pith citing papers