Paper Citation Record · LEDGER

MimickNet, Matching Clinical Post-Processing Under Realistic Black-Box Constraints

As of 16 August 2026, this Paper Citation Record lists 38 of 38 outbound references and 0 inbound Pith citation observations for arXiv:1908.05782.

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

pith.paper-citation-record.v1
1908.05782 v1

Coverage vector

measured 38 of 38 reference resolution

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Pith citing papers itemized under the disclosed page cap.

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Source: cited_works

Reference resolution

38 of 38 outbound references displayed

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External citation measurements

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Outbound references

Observation 3bbf7545-c17d-44e0-809e-dc0ec8795925 · outbound

This paper cites Sources of image degradation in fundamental and harmonic ultrasound imaging using nonlinear, full- wave simulations,.

MimickNet, Matching Clinical Post-Processing Under Realistic Black-Box Constraints Sources of image degradation in fundamental and harmonic ultrasound imaging using nonlinear, full- wave simulations,

Reference 1

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Observation 0ace6f83-7c36-4a74-afc7-eff52a022b1a · outbound

This paper cites Exploring nsight imaging, a totally new architecture for premium ultrasound,.

MimickNet, Matching Clinical Post-Processing Under Realistic Black-Box Constraints Exploring nsight imaging, a totally new architecture for premium ultrasound,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:08:30.872986Z

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This paper cites REFoCUS: Ultrasound focusing for the software beam- forming age,.

MimickNet, Matching Clinical Post-Processing Under Realistic Black-Box Constraints REFoCUS: Ultrasound focusing for the software beam- forming age,

Reference 3

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Observation 8770e9ae-f095-4d07-8ad2-bbeaa764b1cf · outbound

This paper cites Speckle reduction achievable by spatial compounding and frequency compound- ing: Experimental results and implications for target detectability,.

MimickNet, Matching Clinical Post-Processing Under Realistic Black-Box Constraints Speckle reduction achievable by spatial compounding and frequency compound- ing: Experimental results and implications for target detectability,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:08:30.847627Z

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Observation 2e4ef3fd-8d4d-41a6-8a40-24f8151f6f74 · outbound

This paper cites A primer on the physical principles of tissue harmonic imaging,.

MimickNet, Matching Clinical Post-Processing Under Realistic Black-Box Constraints A primer on the physical principles of tissue harmonic imaging,

Reference 5

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Observation 3f1962b6-8b62-4a15-91bb-6b10315691c2 · outbound

This paper cites Clinical utility of fetal Short-Lag spatial coherence imaging,.

MimickNet, Matching Clinical Post-Processing Under Realistic Black-Box Constraints Clinical utility of fetal Short-Lag spatial coherence imaging,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:08:30.517512Z

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Observation 6d387406-4bf8-4521-8966-1c134b375613 · outbound

This paper cites Short-lag spatial coherence imaging in 1.5-d and 1.75-d arrays: Elevation performance and array design considerations,.

MimickNet, Matching Clinical Post-Processing Under Realistic Black-Box Constraints Short-lag spatial coherence imaging in 1.5-d and 1.75-d arrays: Elevation performance and array design considerations,

Reference 7

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This paper cites Understand- ing the advanced signal processing technique of Real-Time adaptive ﬁlters,.

MimickNet, Matching Clinical Post-Processing Under Realistic Black-Box Constraints Understand- ing the advanced signal processing technique of Real-Time adaptive ﬁlters,

Reference 8

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Observation 52a68dd6-3ed7-4a5d-904e-1e84d24beb6a · outbound

This paper cites Deep convolutional neural network for image deconvolution,.

MimickNet, Matching Clinical Post-Processing Under Realistic Black-Box Constraints Deep convolutional neural network for image deconvolution,

Reference 9

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This paper cites Least squares generative adversarial networks,.

MimickNet, Matching Clinical Post-Processing Under Realistic Black-Box Constraints Least squares generative adversarial networks,

Reference 10

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Observation e340fe80-2dd7-413e-92eb-69fb8c900903 · outbound

This paper cites Robust kernel regression for restoration and reconstruction of images from sparse noisy data,.

MimickNet, Matching Clinical Post-Processing Under Realistic Black-Box Constraints Robust kernel regression for restoration and reconstruction of images from sparse noisy data,

Reference 11

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This paper cites Bilateral ﬁltering for gray and color images,.

MimickNet, Matching Clinical Post-Processing Under Realistic Black-Box Constraints Bilateral ﬁltering for gray and color images,

Reference 12

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

MimickNet, Matching Clinical Post-Processing Under Realistic Black-Box Constraints U-net: Convolutional networks for biomedical image segmentation,

Reference 13

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Observation 755fc7d5-d3b2-4995-9792-b35cbbef51a9 · outbound

This paper cites Fast and accurate image super resolution by deep CNN with skip connection and network in network,.

MimickNet, Matching Clinical Post-Processing Under Realistic Black-Box Constraints Fast and accurate image super resolution by deep CNN with skip connection and network in network,

Reference 14

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This paper cites Deep residual learning for image recognition,.

MimickNet, Matching Clinical Post-Processing Under Realistic Black-Box Constraints Deep residual learning for image recognition,

Reference 15

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This paper cites Road extraction by deep residual U- Net,.

MimickNet, Matching Clinical Post-Processing Under Realistic Black-Box Constraints Road extraction by deep residual U- Net,

Reference 16

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Observation e09bb33a-3e31-438d-94aa-7f3976623a0f · outbound

This paper cites Multi-level Wavelet- CNN for image restoration,.

MimickNet, Matching Clinical Post-Processing Under Realistic Black-Box Constraints Multi-level Wavelet- CNN for image restoration,

Reference 17

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This paper cites Densely connected convolutional networks,.

MimickNet, Matching Clinical Post-Processing Under Realistic Black-Box Constraints Densely connected convolutional networks,

Reference 18

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This paper cites The one hundred layers tiramisu: Fully convolutional densenets for semantic segmentation,.

MimickNet, Matching Clinical Post-Processing Under Realistic Black-Box Constraints The one hundred layers tiramisu: Fully convolutional densenets for semantic segmentation,

Reference 19

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This paper cites Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion,.

MimickNet, Matching Clinical Post-Processing Under Realistic Black-Box Constraints Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion,

Reference 20

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

MimickNet, Matching Clinical Post-Processing Under Realistic Black-Box Constraints Loss functions for image restoration with neural networks,

Reference 21

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This paper cites Learning to generate images with perceptual similarity metrics,.

MimickNet, Matching Clinical Post-Processing Under Realistic Black-Box Constraints Learning to generate images with perceptual similarity metrics,

Reference 22

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This paper cites Large scale GAN training for high ﬁdelity natural image synthesis,.

MimickNet, Matching Clinical Post-Processing Under Realistic Black-Box Constraints Large scale GAN training for high ﬁdelity natural image synthesis,

Reference 23

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MimickNet, Matching Clinical Post-Processing Under Realistic Black-Box Constraints Generative adversarial nets,

Reference 24

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This paper cites Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks.

MimickNet, Matching Clinical Post-Processing Under Realistic Black-Box Constraints Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks

Reference 25

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MimickNet, Matching Clinical Post-Processing Under Realistic Black-Box Constraints Image-to-image translation with conditional adversarial networks,

Reference 26

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This paper cites Ultrasound image enhancement using a deep learning architecture,.

MimickNet, Matching Clinical Post-Processing Under Realistic Black-Box Constraints Ultrasound image enhancement using a deep learning architecture,

Reference 27

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This paper cites Ultrasound speckle reduction using generative adversial networks,.

MimickNet, Matching Clinical Post-Processing Under Realistic Black-Box Constraints Ultrasound speckle reduction using generative adversial networks,

Reference 28

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This paper cites Deep convolutional neural network for ultrasound image enhancement,.

MimickNet, Matching Clinical Post-Processing Under Realistic Black-Box Constraints Deep convolutional neural network for ultrasound image enhancement,

Reference 29

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MimickNet, Matching Clinical Post-Processing Under Realistic Black-Box Constraints Unpaired image-to-image translation using cycle-consistent adversarial networks,

Reference 30

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This paper cites In vivo application of short-lag spatial coherence and harmonic spatial coherence imaging in fetal ultrasound,.

MimickNet, Matching Clinical Post-Processing Under Realistic Black-Box Constraints In vivo application of short-lag spatial coherence and harmonic spatial coherence imaging in fetal ultrasound,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:08:29.177306Z

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This paper cites Quantifying image quality improvement using elevated acoustic output in B-Mode harmonic imaging,.

MimickNet, Matching Clinical Post-Processing Under Realistic Black-Box Constraints Quantifying image quality improvement using elevated acoustic output in B-Mode harmonic imaging,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:08:29.165168Z

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Observation 7f5e399c-439f-4fc1-9f4c-2359807fae8a · outbound

This paper cites Implications of lag-one coherence on real-time adaptive frequency selection,.

MimickNet, Matching Clinical Post-Processing Under Realistic Black-Box Constraints Implications of lag-one coherence on real-time adaptive frequency selection,

Reference 33

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This paper cites Image quality assessment: from error visibility to structural similarity,.

MimickNet, Matching Clinical Post-Processing Under Realistic Black-Box Constraints Image quality assessment: from error visibility to structural similarity,

Reference 34

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This paper cites Mo- bilenetv2: Inverted residuals and linear bottlenecks,.

MimickNet, Matching Clinical Post-Processing Under Realistic Black-Box Constraints Mo- bilenetv2: Inverted residuals and linear bottlenecks,

Reference 35

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This paper cites Progressive growing of GANs for improved quality, stability, and variation,.

MimickNet, Matching Clinical Post-Processing Under Realistic Black-Box Constraints Progressive growing of GANs for improved quality, stability, and variation,

Reference 36

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Observation 35b9a589-eaed-421b-a688-e3b8ebe53f79 · outbound

This paper cites Which training methods for GANs do actually converge?.

MimickNet, Matching Clinical Post-Processing Under Realistic Black-Box Constraints Which training methods for GANs do actually converge?

Reference 37

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Observation 5dea1a9c-f6d2-4d59-96ea-da67f50366aa · outbound

This paper cites Mobile ultrafast ultrasound imaging system based on smartphone and tablet devices,.

MimickNet, Matching Clinical Post-Processing Under Realistic Black-Box Constraints Mobile ultrafast ultrasound imaging system based on smartphone and tablet devices,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:08:28.958103Z

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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Pith citing papers

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