Paper Citation Record · LEDGER

Laparoscopic Image Desmoking Using the U-Net with New Loss Function and Integrated Differentiable Wiener Filter

As of 19 August 2026, this Paper Citation Record lists 21 of 21 outbound references and 1 inbound Pith citation observation for arXiv:2505.21634.

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

pith.paper-citation-record.v1
2505.21634 v1

Coverage vector

measured 21 of 21 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T13:30:20.569910Z

measured 22 of 22 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-04T18:38:56.166142Z

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

21 of 21 outbound references displayed

verified exact0
verified fuzzy15
unresolved6
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation a1565a55-ff17-485b-89ce-5fbade83a081 · outbound

This paper cites Removal of smoke effects in laparoscopic surgery via adversarial neural network and the dark channel prior,.

Laparoscopic Image Desmoking Using the U-Net with New Loss Function and Integrated Differentiable Wiener Filter Removal of smoke effects in laparoscopic surgery via adversarial neural network and the dark channel prior,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:30:24.377113Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 15ca30d8-56b9-4197-b497-7ee297bbfa3d · outbound

This paper cites A new benchmark in vivo paired dataset for laparoscopic image de-smoking,.

Laparoscopic Image Desmoking Using the U-Net with New Loss Function and Integrated Differentiable Wiener Filter A new benchmark in vivo paired dataset for laparoscopic image de-smoking,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:30:24.237723Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 56ee79b9-d189-46b7-b5b1-3885a1159e70 · outbound

This paper cites Desmoke-lap: improved unpaired image-to-image translation for desmoking in laparoscopic surgery,.

Laparoscopic Image Desmoking Using the U-Net with New Loss Function and Integrated Differentiable Wiener Filter Desmoke-lap: improved unpaired image-to-image translation for desmoking in laparoscopic surgery,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:30:24.071746Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 29883670-54bd-41d7-abf0-6e9cc2ff5373 · outbound

This paper cites Interpretable automatic rosacea detection with whitened cosine similarity,.

Laparoscopic Image Desmoking Using the U-Net with New Loss Function and Integrated Differentiable Wiener Filter Interpretable automatic rosacea detection with whitened cosine similarity,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:30:23.932762Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 6b3e8a2e-ed5b-4cdb-8ba7-813a33135587 · outbound

This paper cites Skin disease detection using deep learning,.

Laparoscopic Image Desmoking Using the U-Net with New Loss Function and Integrated Differentiable Wiener Filter Skin disease detection using deep learning,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:30:23.736060Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 310ae733-5126-4ccf-a0d6-e1ae49d0fa30 · outbound

This paper cites Increasing rosacea awareness among population using deep learning and statistical approaches,.

Laparoscopic Image Desmoking Using the U-Net with New Loss Function and Integrated Differentiable Wiener Filter Increasing rosacea awareness among population using deep learning and statistical approaches,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:30:23.514903Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 0b60cf2d-0b1f-4b89-bf6f-cd227c76bac4 · outbound

This paper cites U-net: Convolutional networks for biomedical image segmentation,.

Laparoscopic Image Desmoking Using the U-Net with New Loss Function and Integrated Differentiable Wiener Filter U-net: Convolutional networks for biomedical image segmentation,

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-07T13:30:18.949348Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 645d1453-887e-428a-9e25-5f63c20d3da2 · outbound

This paper cites Deep wiener deconvolution: Wiener meets deep learning for image deblurring,.

Laparoscopic Image Desmoking Using the U-Net with New Loss Function and Integrated Differentiable Wiener Filter Deep wiener deconvolution: Wiener meets deep learning for image deblurring,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:30:23.217632Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation b2657e23-7db7-48cf-a70a-c5701ec51c75 · outbound

This paper cites Loss functions for image restoration with neural networks,.

Laparoscopic Image Desmoking Using the U-Net with New Loss Function and Integrated Differentiable Wiener Filter Loss functions for image restoration with neural networks,

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-07T13:30:19.104861Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bd64fe18-e825-4e68-83e0-4c0e2fdd1fa0 · outbound

This paper cites Perceptual losses for real-time style transfer and super-resolution,.

Laparoscopic Image Desmoking Using the U-Net with New Loss Function and Integrated Differentiable Wiener Filter Perceptual losses for real-time style transfer and super-resolution,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:30:22.895093Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 4d03c7c8-38a1-44e3-9f92-8f52abb55527 · outbound

This paper cites Single image haze removal using dark channel prior,.

Laparoscopic Image Desmoking Using the U-Net with New Loss Function and Integrated Differentiable Wiener Filter Single image haze removal using dark channel prior,

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-07T13:30:19.359857Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cb78980f-b773-4499-9bde-78dbef7da87a · outbound

This paper cites Generative smoke removal,.

Laparoscopic Image Desmoking Using the U-Net with New Loss Function and Integrated Differentiable Wiener Filter Generative smoke removal,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:30:22.672039Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 3e43ba1b-0b7d-4435-be36-499d144e0c6a · outbound

This paper cites Multi-stages de-smoking model based on cyclegan for surgical de-smoking,.

Laparoscopic Image Desmoking Using the U-Net with New Loss Function and Integrated Differentiable Wiener Filter Multi-stages de-smoking model based on cyclegan for surgical de-smoking,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:30:22.379647Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 3e9ecb3a-237f-484d-888c-30b396b79f3c · outbound

This paper cites Desmoking laparoscopy surgery images using an image-to-image translation guided by an embedded dark channel,.

Laparoscopic Image Desmoking Using the U-Net with New Loss Function and Integrated Differentiable Wiener Filter Desmoking laparoscopy surgery images using an image-to-image translation guided by an embedded dark channel,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:30:22.078233Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 874df568-edca-4d83-a9db-faccb73953d9 · outbound

This paper cites Vision transformers for single image dehazing,.

Laparoscopic Image Desmoking Using the U-Net with New Loss Function and Integrated Differentiable Wiener Filter Vision transformers for single image dehazing,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:30:21.833267Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 8cd25e21-2714-401a-830c-7051e18ed3f1 · outbound

This paper cites Medical image segmentation review: The success of u-net,.

Laparoscopic Image Desmoking Using the U-Net with New Loss Function and Integrated Differentiable Wiener Filter Medical image segmentation review: The success of u-net,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:30:21.608454Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 884ec2c1-ff48-4588-a18d-b45e76146a18 · outbound

This paper cites an unresolved cited work.

Laparoscopic Image Desmoking Using the U-Net with New Loss Function and Integrated Differentiable Wiener Filter Unresolved cited work

Reference 17

Resolution: unresolved; raw_fallback, observed 2026-08-07T13:30:21.385316Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 05a0b2f8-77f6-4166-b492-6b6eca5d521e · outbound

This paper cites Evaluation of ssim loss function in rir generator gans,.

Laparoscopic Image Desmoking Using the U-Net with New Loss Function and Integrated Differentiable Wiener Filter Evaluation of ssim loss function in rir generator gans,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:30:21.168574Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation a317ea47-3ec5-42d6-9117-e0e9b7f8e9d2 · outbound

This paper cites Comparison of the cielab and ciede2000 color difference formulas,.

Laparoscopic Image Desmoking Using the U-Net with New Loss Function and Integrated Differentiable Wiener Filter Comparison of the cielab and ciede2000 color difference formulas,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:30:20.966531Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 28949980-1a70-4307-9f52-5673ec9f28ad · outbound

This paper cites Very Deep Convolutional Networks for Large-Scale Image Recognition.

Laparoscopic Image Desmoking Using the U-Net with New Loss Function and Integrated Differentiable Wiener Filter Very Deep Convolutional Networks for Large-Scale Image Recognition

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-07T13:30:20.445028Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 910304d8-3df8-411d-bc4d-e9f20d5d2a2f · outbound

This paper cites Image-to-image translation with conditional adversarial networks,.

Laparoscopic Image Desmoking Using the U-Net with New Loss Function and Integrated Differentiable Wiener Filter Image-to-image translation with conditional adversarial networks,

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-07T13:30:20.569910Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation e1bb6ad6-b354-47fd-96a0-55a83ffec794 · inbound

Investigating the Impact of Various Loss Functions and Learnable Wiener Filter for Laparoscopic Image Desmoking cites this paper.

Investigating the Impact of Various Loss Functions and Learnable Wiener Filter for Laparoscopic Image Desmoking Laparoscopic Image Desmoking Using the U-Net with New Loss Function and Integrated Differentiable Wiener Filter

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-04T18:38:56.166142Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.