Pith. sign in

Paper Citation Record · LEDGER

Weakly-Convex Regularization for Magnetic Resonance Image Denoising

As of 20 August 2026, this Paper Citation Record lists 24 of 24 outbound references and 0 inbound Pith citation observations for arXiv:2508.14438.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2508.14438 v1

Coverage vector

measured 24 of 24 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T18:35:53.844251Z

measured 24 of 24 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

24 of 24 outbound references displayed

  • verified exact1
  • verified fuzzy21
  • unresolved2
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 5aa0e883-6664-4813-9a57-4b4699ad1dd8 · outbound

This paper cites On the design of weakly-convex regularizers for solving linear inverse problems,.

Weakly-Convex Regularization for Magnetic Resonance Image Denoising On the design of weakly-convex regularizers for solving linear inverse problems,

Reference 1

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T18:35:57.001367Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-05T18:35:52.135204Z digest=sha256:bdbc319cb699786b5b58568429c18afaa57dcf2f4f215987cf59fad870ebe10c

Observation ec164872-83bf-478e-a896-2346de0a01b0 · outbound

This paper cites Plug-and-play priors for model based reconstruction,.

Weakly-Convex Regularization for Magnetic Resonance Image Denoising Plug-and-play priors for model based reconstruction,

Reference 2

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T18:35:56.868207Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-05T18:35:52.223146Z digest=sha256:fbf9ca56c7fe0820650adefda32513d220ec30648ee910e260a85e7a185214d2

Observation 79bfd49e-8b11-4b16-ad96-7fc9dee0e0a0 · outbound

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

Weakly-Convex Regularization for Magnetic Resonance Image Denoising Plug-and-play methods for integrating physical and learned models in computational imaging: Theory, algorithms, and applications,

Reference 3

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T18:35:56.686038Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-05T18:35:52.299359Z digest=sha256:9e474f12cb3e593c04d51400b3dfebc6f8795a770c04ab884963bdf2f87923b5

Observation 172d54a9-472f-4b80-bb63-76c7e0dd4298 · outbound

This paper cites Plug-and- play methods provably converge with properly trained denoisers,.

Weakly-Convex Regularization for Magnetic Resonance Image Denoising Plug-and- play methods provably converge with properly trained denoisers,

Reference 4

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T18:35:56.554165Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-05T18:35:52.382264Z digest=sha256:ed459555a258e2f6cbe8f772a98b0864e5488aa77197c75601b838d2fce388fc

Observation eec19378-217a-4c9e-acb6-b0d106e2d500 · outbound

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

Weakly-Convex Regularization for Magnetic Resonance Image Denoising Plug- and-play image restoration with deep denoiser prior,

Reference 5

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T18:35:56.436440Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-05T18:35:52.462363Z digest=sha256:22564fe6d8816c13670660c85513731c9c75a02a6f7132c6a72507fdcb74edf9

Observation b7b10beb-f574-4f5d-b07a-0c14d7458f66 · outbound

This paper cites Lanza, S.

Weakly-Convex Regularization for Magnetic Resonance Image Denoising Lanza, S

Reference 6

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T18:35:56.322305Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-05T18:35:52.530998Z digest=sha256:f5b80874a8351e8e7a4e12fc437ccc0a144110ea125fc5b95d55af69509096c0

Observation 17915b2b-8a05-4c91-83c1-c0820aca53bf · outbound

This paper cites Learning weakly convex regularizers for convergent image-reconstruction algorithms,.

Weakly-Convex Regularization for Magnetic Resonance Image Denoising Learning weakly convex regularizers for convergent image-reconstruction algorithms,

Reference 7

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T18:35:56.189408Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-05T18:35:52.636841Z digest=sha256:0c952edbae58316ac0e6ae7de9fc5016537510d597871f2430816718e966fccd

Observation 96d485fc-fc7d-4a44-9385-7868b56fcb88 · outbound

This paper cites Weakly Convex Regularisers for Inverse Problems: Convergence of Critical Points and Primal-Dual Optimisation.

Weakly-Convex Regularization for Magnetic Resonance Image Denoising Weakly Convex Regularisers for Inverse Problems: Convergence of Critical Points and Primal-Dual Optimisation

Reference 8

Resolution
verified exact
local_arxiv, observed 2026-08-05T18:35:54.023222Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-05T18:35:52.729562Z digest=sha256:4e03a820a4ee2ea56325c36a46dc9358c34a6b1cadbf66639f3fed796601e44d

Observation ddf3708f-2007-4a2c-86b7-01a24f15780e · outbound

This paper cites An ensemble of proximal networks for sparse coding,.

Weakly-Convex Regularization for Magnetic Resonance Image Denoising An ensemble of proximal networks for sparse coding,

Reference 9

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T18:35:56.077005Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-05T18:35:52.834992Z digest=sha256:d8b349f22028d0a0c941cc7e71af62e28859551b3241cc4c2c9428a4b2607f35

Observation cbae9926-610c-4e58-ab8d-5bf3572cfd17 · outbound

This paper cites FirmNet: A sparsity amplified deep network for solving linear inverse problems,.

Weakly-Convex Regularization for Magnetic Resonance Image Denoising FirmNet: A sparsity amplified deep network for solving linear inverse problems,

Reference 10

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T18:35:55.945786Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-05T18:35:52.907863Z digest=sha256:942b82f629f79b34ffda94ec7f869209ce56ebcc2b92f272b172d29e863ec497

Observation 264823e6-f6c9-49f0-a4ae-f28ce1ee7f67 · outbound

This paper cites Iteratively reweighted minimax-concave penalty minimization for accurate low- rank plus sparse matrix decomposition,.

Weakly-Convex Regularization for Magnetic Resonance Image Denoising Iteratively reweighted minimax-concave penalty minimization for accurate low- rank plus sparse matrix decomposition,

Reference 11

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T18:35:55.781302Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-05T18:35:52.991188Z digest=sha256:7c644ce5414a7fa31853e0e068050c936e164e43b866e116b5fc87902f829e57

Observation 20047bc1-27ff-418a-a348-3afe0c17c406 · outbound

This paper cites an unresolved cited work.

Weakly-Convex Regularization for Magnetic Resonance Image Denoising Unresolved cited work

Reference 12

Resolution
unresolved
raw_fallback, observed 2026-08-05T18:35:55.607123Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-05T18:35:53.051664Z digest=sha256:d048ffe27d6b21b1ae2bd102fa5acd201f9ff0152ed4c516d904fd416543a23c

Observation 70b40220-0092-4069-944b-cb89d5839e6d · outbound

This paper cites Proximal algorithms,.

Weakly-Convex Regularization for Magnetic Resonance Image Denoising Proximal algorithms,

Reference 13

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T18:35:55.498389Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-05T18:35:53.136657Z digest=sha256:a3aade75afb08173874a469d2210f06c71f29c97637d48751617ee8a8902e06d

Observation 068b6c25-0156-41d2-bfc8-2244203b0788 · outbound

This paper cites Beck,First-order Methods in Optimization.

Weakly-Convex Regularization for Magnetic Resonance Image Denoising Beck,First-order Methods in Optimization

Reference 14

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T18:35:55.417830Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-05T18:35:53.220653Z digest=sha256:c5072e83b95f5f0405cc983b5d531ea7727b1bece52cbddc4ed61e8efe8dd5a9

Observation 4ea67165-3480-4700-b9c6-663964abc3e5 · outbound

This paper cites Nearly unbiased variable selection under minimax con- cave penalty,.

Weakly-Convex Regularization for Magnetic Resonance Image Denoising Nearly unbiased variable selection under minimax con- cave penalty,

Reference 15

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T18:35:55.265540Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-05T18:35:53.272153Z digest=sha256:1c3b9df20edd74da176530bb474365689413fdd9716ec259ca37475297e207ea

Observation 1a66c0fd-8cb2-4a24-90b4-03e87255d11d · outbound

This paper cites Variable selection via nonconcave penalized likelihood and its oracle properties,.

Weakly-Convex Regularization for Magnetic Resonance Image Denoising Variable selection via nonconcave penalized likelihood and its oracle properties,

Reference 16

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T18:35:55.144538Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-05T18:35:53.385494Z digest=sha256:01f5985fbf3dbf18d54deb493070a501796c946ce94e5112eb5876b2350cd53b

Observation 07bde6f1-43bd-44ea-bfda-d1cd41ab5bf4 · outbound

This paper cites Techniques for nonlinear least squares and robust regression,.

Weakly-Convex Regularization for Magnetic Resonance Image Denoising Techniques for nonlinear least squares and robust regression,

Reference 17

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T18:35:55.017988Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-05T18:35:53.444631Z digest=sha256:71fb9f955663a3eccb5e88b05b9f4666e3c9171408ca01e372b3cc4409c6c3a2

Observation ec5f6f70-9f39-4712-bbb5-8bb462186f8f · outbound

This paper cites Implicit neural representations with periodic activation functions,.

Weakly-Convex Regularization for Magnetic Resonance Image Denoising Implicit neural representations with periodic activation functions,

Reference 18

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T18:35:54.869652Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-05T18:35:53.498332Z digest=sha256:3395a2aba16c1afffaa1aff15ff4d690d0869c0235b8b40a66934401ad80181d

Observation 4a981f39-7d30-4186-b608-a9508996c200 · outbound

This paper cites WIRE: Wavelet implicit neural representations,.

Weakly-Convex Regularization for Magnetic Resonance Image Denoising WIRE: Wavelet implicit neural representations,

Reference 19

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T18:35:54.750983Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-05T18:35:53.572936Z digest=sha256:a06036c7ddedaa96260f3ed3ba43b00f0c377055069a7fb39760aa65810d72a6

Observation aa394b1d-c866-4b36-8f06-909a2c357997 · outbound

This paper cites Improving fiber alignment in HARDI by combining contextual PDE flow with constrained spherical deconvolution,.

Weakly-Convex Regularization for Magnetic Resonance Image Denoising Improving fiber alignment in HARDI by combining contextual PDE flow with constrained spherical deconvolution,

Reference 20

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T18:35:54.590219Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-05T18:35:53.626828Z digest=sha256:f989f4760602e3ad400d3fb8ae789a8943d66f4e6b594fe0e7a71d4866f99b04

Observation 241495f4-00b8-4c26-a247-e4f6577fd6ce · outbound

This paper cites MR diffusion tensor spectroscopy and imaging,.

Weakly-Convex Regularization for Magnetic Resonance Image Denoising MR diffusion tensor spectroscopy and imaging,

Reference 21

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T18:35:54.432000Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-05T18:35:53.667835Z digest=sha256:c3a1e040689447cbee287a81f339367d53121ce6d5dcd9fe7d13bab14623a06f

Observation 50addad6-f11b-4325-81a9-cee957ae7b97 · outbound

This paper cites High angular res- olution diffusion MRI.

Weakly-Convex Regularization for Magnetic Resonance Image Denoising High angular res- olution diffusion MRI

Reference 22

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T18:35:54.276468Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-05T18:35:53.718293Z digest=sha256:c73b1768a48938b5b3ee1c9c0ee4fbfd663009aaa7b7271b2acc9b3442d7ecd5

Observation 2e1a7981-239f-4d81-a986-64372158ac0d · outbound

This paper cites Algorithm unrolling: Interpretable, efficient deep learning for signal and image processing,.

Weakly-Convex Regularization for Magnetic Resonance Image Denoising Algorithm unrolling: Interpretable, efficient deep learning for signal and image processing,

Reference 23

Resolution
unresolved
no resolver link, observed 2026-08-05T18:35:53.792472Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T18:35:53.792472Z digest=sha256:bbd391ced358bb39d9d097a5a9d0ffbb3d32c7474f4112e28c1aef90007374d6

Observation 0ee2ef78-b56f-4296-958b-673610b084ec · outbound

This paper cites Patch2Self: Denoising diffusion MRI with self-supervised learning,.

Weakly-Convex Regularization for Magnetic Resonance Image Denoising Patch2Self: Denoising diffusion MRI with self-supervised learning,

Reference 24

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T18:35:54.153938Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-05T18:35:53.844251Z digest=sha256:81260b52effd60b493971f55a2fdbbc721e011e7bb7bd5f207812c2599b62288

Pith citing papers

No inbound Pith citation observations are available.