Paper Citation Record · LEDGER

SROBB: Targeted Perceptual Loss for Single Image Super-Resolution

As of 19 August 2026, this Paper Citation Record lists 46 of 46 outbound references and 0 inbound Pith citation observations for arXiv:1908.07222.

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

pith.paper-citation-record.v1
1908.07222 v1

Coverage vector

measured 46 of 46 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-14T12:25:52.929725Z

measured 46 of 46 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 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

46 of 46 outbound references displayed

verified exact2
verified fuzzy27
unresolved17
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 2ca03489-0624-413a-a844-ba1cc1b651a1 · outbound

This paper cites Low-Complexity Single-Image Super-Resolution based on Nonnegative Neighbor Embed- ding.

SROBB: Targeted Perceptual Loss for Single Image Super-Resolution Low-Complexity Single-Image Super-Resolution based on Nonnegative Neighbor Embed- ding

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:25:54.033304Z

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 b64598aa-7ba8-4e33-9613-20becacce3b8 · outbound

This paper cites The 2018 PIRM Challenge on Perceptual Image Super-resolution.

SROBB: Targeted Perceptual Loss for Single Image Super-Resolution The 2018 PIRM Challenge on Perceptual Image Super-resolution

Reference 2

Resolution: verified exact; local_arxiv, observed 2026-08-14T12:25:53.164836Z

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 8c3d008d-58bb-45fb-b9fa-161bd8a0ce63 · outbound

This paper cites Super-Resolution with Deep Convolutional Sufficient Statistics.

SROBB: Targeted Perceptual Loss for Single Image Super-Resolution Super-Resolution with Deep Convolutional Sufficient Statistics

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-14T12:25:52.665166Z

Source-reported events for the cited work

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Observation fc6e4adf-e1b2-478e-ba1f-aef004d9a5ac · outbound

This paper cites an unresolved cited work.

SROBB: Targeted Perceptual Loss for Single Image Super-Resolution Unresolved cited work

Reference 4

Resolution: unresolved; raw_fallback, observed 2026-08-14T12:25:54.008127Z

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 67690ea9-45eb-472c-b729-4cb53dec0747 · outbound

This paper cites Image super-resolution using deep convolutional net- works.

SROBB: Targeted Perceptual Loss for Single Image Super-Resolution Image super-resolution using deep convolutional net- works

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:25:53.987140Z

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 ed0dde7b-24f1-4892-ac2e-0fd6799fcd15 · outbound

This paper cites Learning a deep convolutional network for im- age super-resolution.

SROBB: Targeted Perceptual Loss for Single Image Super-Resolution Learning a deep convolutional network for im- age super-resolution

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:25:53.966919Z

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 06003fee-9184-4ce1-a17b-75234afd5bed · outbound

This paper cites an unresolved cited work.

SROBB: Targeted Perceptual Loss for Single Image Super-Resolution Unresolved cited work

Reference 7

Resolution: unresolved; raw_fallback, observed 2026-08-14T12:25:53.939672Z

Source-reported events for the cited work

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Observation 25fe3fbf-83ab-42a8-a076-25a2225876a7 · outbound

This paper cites A Neural Algorithm of Artistic Style.

SROBB: Targeted Perceptual Loss for Single Image Super-Resolution A Neural Algorithm of Artistic Style

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-14T12:25:52.692540Z

Source-reported events for the cited work

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Observation 089268f5-4ebf-40e5-9fd4-0ed56bac31cc · outbound

This paper cites Texture Synthesis Using Convolutional Neural Networks.

SROBB: Targeted Perceptual Loss for Single Image Super-Resolution Texture Synthesis Using Convolutional Neural Networks

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-14T12:25:52.698152Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f72382d1-19d8-4e17-a064-18dea39a39ae · outbound

This paper cites The unreasonable effectiveness of texture transfer for single image super-resolution.

SROBB: Targeted Perceptual Loss for Single Image Super-Resolution The unreasonable effectiveness of texture transfer for single image super-resolution

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:25:53.917583Z

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 9c8f3d4d-7e37-49b4-87e3-4338c75079aa · outbound

This paper cites Generative adversarial nets.

SROBB: Targeted Perceptual Loss for Single Image Super-Resolution Generative adversarial nets

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-14T12:25:52.710228Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 59f21618-a38b-4111-95be-9aaff6d71143 · outbound

This paper cites Deep residual learning for image recognition.

SROBB: Targeted Perceptual Loss for Single Image Super-Resolution Deep residual learning for image recognition

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:25:53.885542Z

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 face3078-33c5-444b-bf04-9e96b655cb67 · outbound

This paper cites Identity mappings in deep residual networks.

SROBB: Targeted Perceptual Loss for Single Image Super-Resolution Identity mappings in deep residual networks

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:25:53.866192Z

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 f63247f9-5dbf-45bf-8aca-ffa3d9588880 · outbound

This paper cites Sin- gle image super-resolution from transformed self-exemplars.

SROBB: Targeted Perceptual Loss for Single Image Super-Resolution Sin- gle image super-resolution from transformed self-exemplars

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:25:53.847630Z

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 e9c11728-510f-466e-acba-4385b26e6ad5 · outbound

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

SROBB: Targeted Perceptual Loss for Single Image Super-Resolution Perceptual losses for real-time style transfer and super-resolution

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-14T12:25:52.736289Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3dc8fc40-1fb1-41cd-85b5-3d2df2bc6e0e · outbound

This paper cites Accu- rate image super-resolution using very deep convolutional networks.

SROBB: Targeted Perceptual Loss for Single Image Super-Resolution Accu- rate image super-resolution using very deep convolutional networks

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:25:53.808063Z

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 bf04b299-0811-42fe-9f95-9e39b29579e1 · outbound

This paper cites Deeply- recursive convolutional network for image super-resolution.

SROBB: Targeted Perceptual Loss for Single Image Super-Resolution Deeply- recursive convolutional network for image super-resolution

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:25:53.785543Z

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 9e663cad-4c83-416d-b522-d9b9d448e88d · outbound

This paper cites Adam: A method for stochastic optimization.

SROBB: Targeted Perceptual Loss for Single Image Super-Resolution Adam: A method for stochastic optimization

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:25:53.758960Z

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 1b8b4398-653f-4885-b350-1df5c03422c8 · outbound

This paper cites Deep laplacian pyramid networks for fast and accurate super-resolution.

SROBB: Targeted Perceptual Loss for Single Image Super-Resolution Deep laplacian pyramid networks for fast and accurate super-resolution

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:25:53.736995Z

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 8aebdc6d-5ff6-4b77-afc0-88f39425c08a · outbound

This paper cites Aitken, Alykhan Tejani, Johannes Totz, Zehan Wang, and Wenzhe Shi.

SROBB: Targeted Perceptual Loss for Single Image Super-Resolution Aitken, Alykhan Tejani, Johannes Totz, Zehan Wang, and Wenzhe Shi

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:25:53.712775Z

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 ce59cf0c-0732-4fe0-bf12-c37f3b550818 · outbound

This paper cites Enhanced deep residual networks for single image super-resolution.

SROBB: Targeted Perceptual Loss for Single Image Super-Resolution Enhanced deep residual networks for single image super-resolution

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:25:53.690110Z

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 b9057170-67ff-4e0c-9d9f-c565ac22943a · outbound

This paper cites Microsoft coco: Common objects in context.

SROBB: Targeted Perceptual Loss for Single Image Super-Resolution Microsoft coco: Common objects in context

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-14T12:25:52.777142Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0eabc92c-7b94-4093-8122-20f043beb15d · outbound

This paper cites Visualizing deep convolutional neural networks using natural pre-images.

SROBB: Targeted Perceptual Loss for Single Image Super-Resolution Visualizing deep convolutional neural networks using natural pre-images

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:25:53.651540Z

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 1f83197f-1093-4fec-997e-e2eb8967a03a · outbound

This paper cites Maintaining Natural Image Statistics with the Contextual Loss.

SROBB: Targeted Perceptual Loss for Single Image Super-Resolution Maintaining Natural Image Statistics with the Contextual Loss

Reference 24

Resolution: verified exact; local_arxiv, observed 2026-08-14T12:25:53.058006Z

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 d9079a8f-b489-4b99-96fc-a3e5e2ecbd6b · outbound

This paper cites an unresolved cited work.

SROBB: Targeted Perceptual Loss for Single Image Super-Resolution Unresolved cited work

Reference 25

Resolution: unresolved; raw_fallback, observed 2026-08-14T12:25:53.626137Z

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 9dc207e0-0d2b-4cb3-9fc5-96c255e6d60d · outbound

This paper cites Beneﬁting from multitask learn- ing to improve single image super-resolution.

SROBB: Targeted Perceptual Loss for Single Image Super-Resolution Beneﬁting from multitask learn- ing to improve single image super-resolution

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:25:53.601889Z

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 f3f09864-5959-486a-ba68-fccfdb585299 · outbound

This paper cites an unresolved cited work.

SROBB: Targeted Perceptual Loss for Single Image Super-Resolution Unresolved cited work

Reference 27

Resolution: unresolved; raw_fallback, observed 2026-08-14T12:25:53.581116Z

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 22f2acf9-9109-41c1-9274-0f7cb9249afa · outbound

This paper cites Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps.

SROBB: Targeted Perceptual Loss for Single Image Super-Resolution Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-14T12:25:52.812072Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ffe6fef2-2476-4a0b-8d44-1336905d7a29 · outbound

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

SROBB: Targeted Perceptual Loss for Single Image Super-Resolution Very Deep Convolutional Networks for Large-Scale Image Recognition

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-14T12:25:52.820825Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7d73e70c-bec6-4ca5-a02c-565725a51597 · outbound

This paper cites an unresolved cited work.

SROBB: Targeted Perceptual Loss for Single Image Super-Resolution Unresolved cited work

Reference 30

Resolution: unresolved; raw_fallback, observed 2026-08-14T12:25:53.562228Z

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 88c5dc85-6082-4987-88ca-2c44cc97b58c · outbound

This paper cites Mem- net: A persistent memory network for image restoration.

SROBB: Targeted Perceptual Loss for Single Image Super-Resolution Mem- net: A persistent memory network for image restoration

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:25:53.543253Z

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 f3e3f375-9b6c-4e93-93b4-29d5ff0addd3 · outbound

This paper cites Seman- tic super-resolution: When and where it is useful? Computer Vision and Image Understanding, September 2015.

SROBB: Targeted Perceptual Loss for Single Image Super-Resolution Seman- tic super-resolution: When and where it is useful? Computer Vision and Image Understanding, September 2015

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:25:53.523181Z

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 5dccb262-bf32-46c5-8a70-4d2d6d30b6bc · outbound

This paper cites Tsai and T.

SROBB: Targeted Perceptual Loss for Single Image Super-Resolution Tsai and T

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:25:53.501512Z

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 fa5dd8cc-dc52-4d6d-896d-83eed834bdb7 · outbound

This paper cites an unresolved cited work.

SROBB: Targeted Perceptual Loss for Single Image Super-Resolution Unresolved cited work

Reference 34

Resolution: unresolved; raw_fallback, observed 2026-08-14T12:25:53.479055Z

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 2b5dc6f0-009c-47b8-a135-3b2c012f80f6 · outbound

This paper cites Recovering realistic texture in image super-resolution by deep spatial feature transform.

SROBB: Targeted Perceptual Loss for Single Image Super-Resolution Recovering realistic texture in image super-resolution by deep spatial feature transform

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:25:53.447262Z

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 be1e1750-36b6-418d-8398-dab7431c0197 · outbound

This paper cites Esrgan: En- hanced super-resolution generative adversarial networks.

SROBB: Targeted Perceptual Loss for Single Image Super-Resolution Esrgan: En- hanced super-resolution generative adversarial networks

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:25:53.424264Z

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 9a8a751a-7146-4476-9f62-094e6d557892 · outbound

This paper cites an unresolved cited work.

SROBB: Targeted Perceptual Loss for Single Image Super-Resolution Unresolved cited work

Reference 37

Resolution: unresolved; raw_fallback, observed 2026-08-14T12:25:53.398045Z

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 66cc182a-5d5d-4310-8a84-453b905f5954 · outbound

This paper cites Understanding Neural Networks Through Deep Visualization.

SROBB: Targeted Perceptual Loss for Single Image Super-Resolution Understanding Neural Networks Through Deep Visualization

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-14T12:25:52.878053Z

Source-reported events for the cited work

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Observation a63d245e-db4c-465d-a7fb-c67b279adc1f · outbound

This paper cites Craft- ing a toolchain for image restoration by deep reinforcement learning.

SROBB: Targeted Perceptual Loss for Single Image Super-Resolution Craft- ing a toolchain for image restoration by deep reinforcement learning

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:25:53.366368Z

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 2f33f325-e55d-473b-88b2-6283a5dca2e9 · outbound

This paper cites Visualizing and comparing convolutional neural net- works, 2014.

SROBB: Targeted Perceptual Loss for Single Image Super-Resolution Visualizing and comparing convolutional neural net- works, 2014

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:25:53.336642Z

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 22274766-c896-46b9-8d5a-207c57069299 · outbound

This paper cites Unsupervised image super- resolution using cycle-in-cycle generative adversarial net- works.

SROBB: Targeted Perceptual Loss for Single Image Super-Resolution Unsupervised image super- resolution using cycle-in-cycle generative adversarial net- works

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:25:53.310621Z

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 5edcf251-1d6a-4ee8-b40d-8b9983145cef · outbound

This paper cites On single image scale-up using sparse-representations.

SROBB: Targeted Perceptual Loss for Single Image Super-Resolution On single image scale-up using sparse-representations

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:25:53.286716Z

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 d64ba79d-0978-4e61-b80f-b12214e01751 · outbound

This paper cites Efros, Eli Shecht- man, and Oliver Wang.

SROBB: Targeted Perceptual Loss for Single Image Super-Resolution Efros, Eli Shecht- man, and Oliver Wang

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:25:53.264168Z

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 74a98583-e1dc-42e4-87c0-9ef59a2bdfa9 · outbound

This paper cites Image super-resolution using very deep residual channel attention networks.

SROBB: Targeted Perceptual Loss for Single Image Super-Resolution Image super-resolution using very deep residual channel attention networks

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-14T12:25:52.916789Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 49463827-9c22-4a9b-8f38-332a79d56f54 · outbound

This paper cites Residual dense network for image super-resolution.

SROBB: Targeted Perceptual Loss for Single Image Super-Resolution Residual dense network for image super-resolution

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:25:53.211860Z

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 1fffabaf-3db4-4024-b1ea-d374ea88730d · outbound

This paper cites Scene parsing through ade20k dataset.

SROBB: Targeted Perceptual Loss for Single Image Super-Resolution Scene parsing through ade20k dataset

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:25:53.188609Z

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.

Pith citing papers

No inbound Pith citation observations are available.