Paper Citation Record · LEDGER

Image Quality Enhancement and Detection of Small and Dense Objects in Industrial Recycling Processes

As of 21 August 2026, this Paper Citation Record lists 24 of 24 outbound references and 1 inbound Pith citation observation for arXiv:2509.01332.

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

pith.paper-citation-record.v1
2509.01332 v1

Coverage vector

measured 24 of 24 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T12:42:33.536800Z

measured 25 of 25 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-05T12:42:33.443230Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-05T12:42:33.619198Z

Reference resolution

24 of 24 outbound references displayed

verified exact1
verified fuzzy20
unresolved3
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 8e14dd49-9e6a-4174-8cd1-b08b7cd38fc7 · outbound

This paper cites Image Quality Enhancement and Detection of Small and Dense Objects in Industrial Recycling Processes.

Image Quality Enhancement and Detection of Small and Dense Objects in Industrial Recycling Processes Image Quality Enhancement and Detection of Small and Dense Objects in Industrial Recycling Processes

Reference 1

Resolution: verified exact; local_arxiv, observed 2026-08-05T12:42:33.626041Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 4a6ccb82-1fae-4b8c-84ad-5026253298a3 · outbound

This paper cites This damage can result in dead pixels that consistently output a fixed value.

Image Quality Enhancement and Detection of Small and Dense Objects in Industrial Recycling Processes This damage can result in dead pixels that consistently output a fixed value

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:42:33.904680Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 1b7dc112-a68c-4da7-b90e-f2dd87924c6c · outbound

This paper cites New dataset The dataset for this study was collected using a simula- tion model developed in accordance with recommendations from Orano group.

Image Quality Enhancement and Detection of Small and Dense Objects in Industrial Recycling Processes New dataset The dataset for this study was collected using a simula- tion model developed in accordance with recommendations from Orano group

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:42:33.878212Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation d508fe1f-77eb-4d13-b35a-3b91b51d8a69 · outbound

This paper cites The results from SOTA object detection methods are promising.

Image Quality Enhancement and Detection of Small and Dense Objects in Industrial Recycling Processes The results from SOTA object detection methods are promising

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:42:33.864864Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 9b5551d4-2fbb-4deb-a150-0812aba7b82d · outbound

This paper cites Yolov7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors,.

Image Quality Enhancement and Detection of Small and Dense Objects in Industrial Recycling Processes Yolov7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:42:33.798511Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 9f6bb3c4-0a6e-4d13-8d80-ef9de5b3f5d3 · outbound

This paper cites Object detection with deep learning: A re- view,.

Image Quality Enhancement and Detection of Small and Dense Objects in Industrial Recycling Processes Object detection with deep learning: A re- view,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:42:33.851995Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation d76e73cc-6999-48c3-9a31-c5c907636af7 · outbound

This paper cites Object detection in 20 years: A sur- vey,.

Image Quality Enhancement and Detection of Small and Dense Objects in Industrial Recycling Processes Object detection in 20 years: A sur- vey,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:42:33.839265Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 6c3d3664-58a6-4f8d-9459-2aa16d68f6e6 · outbound

This paper cites Small-object detec- tion in remote sensing images with end-to-end edge- enhanced gan and object detector network,.

Image Quality Enhancement and Detection of Small and Dense Objects in Industrial Recycling Processes Small-object detec- tion in remote sensing images with end-to-end edge- enhanced gan and object detector network,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:42:33.825978Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 95faff97-d7ad-4e64-8f42-9daf6cb72bf4 · outbound

This paper cites Faster r-cnn: Towards real-time object detection with region proposal networks,.

Image Quality Enhancement and Detection of Small and Dense Objects in Industrial Recycling Processes Faster r-cnn: Towards real-time object detection with region proposal networks,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:42:33.812322Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 35484ef7-e73d-46b4-9f21-01aab3d8cc94 · outbound

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

Image Quality Enhancement and Detection of Small and Dense Objects in Industrial Recycling Processes On single image scale-up using sparse-representations,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:42:33.728946Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 09578da1-7b4c-4953-9fd0-d8ae58d43ec4 · outbound

This paper cites Finally, we select 100 images (80% for train, and 20% for test) from our 10k UDD dataset for evaluation.

Image Quality Enhancement and Detection of Small and Dense Objects in Industrial Recycling Processes Finally, we select 100 images (80% for train, and 20% for test) from our 10k UDD dataset for evaluation

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:42:33.891537Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 8f469541-e399-4963-874a-a2a552ca794d · outbound

This paper cites Faster r-cnn: Towards real-time object detection with region proposal networks,.

Image Quality Enhancement and Detection of Small and Dense Objects in Industrial Recycling Processes Faster r-cnn: Towards real-time object detection with region proposal networks,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:42:33.784887Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 24794d9f-4de1-422e-a8db-67e3fe193d02 · outbound

This paper cites Image denoising: The deep learning revolution and be- yond—a survey paper,.

Image Quality Enhancement and Detection of Small and Dense Objects in Industrial Recycling Processes Image denoising: The deep learning revolution and be- yond—a survey paper,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:42:33.771460Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation a22c6020-a8e1-4228-a43c-0a6381361742 · outbound

This paper cites Photon, poisson noise,.

Image Quality Enhancement and Detection of Small and Dense Objects in Industrial Recycling Processes Photon, poisson noise,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:42:33.757032Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 4bf6c589-f640-42bb-8b64-3c9a5f807fad · outbound

This paper cites Salt-and-pepper noise removal by median-type noise detectors and detail-preserving regularization,.

Image Quality Enhancement and Detection of Small and Dense Objects in Industrial Recycling Processes Salt-and-pepper noise removal by median-type noise detectors and detail-preserving regularization,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:42:33.742411Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation cf91a19d-8cce-4ec9-98e8-39eef1e75aa6 · outbound

This paper cites Contour detection and hierarchical im- age segmentation,.

Image Quality Enhancement and Detection of Small and Dense Objects in Industrial Recycling Processes Contour detection and hierarchical im- age segmentation,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:42:33.715171Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 94cefd63-e25f-4c95-a5d7-23f0ba0e3457 · outbound

This paper cites ultralyt- ics/yolov5: v7. 0-yolov5 sota realtime instance segmen- tation,.

Image Quality Enhancement and Detection of Small and Dense Objects in Industrial Recycling Processes ultralyt- ics/yolov5: v7. 0-yolov5 sota realtime instance segmen- tation,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:42:33.700259Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 2d98043b-d177-4051-ba75-c01e8a8388b0 · outbound

This paper cites YOLOv6: A Single-Stage Object Detection Framework for Industrial Applications.

Image Quality Enhancement and Detection of Small and Dense Objects in Industrial Recycling Processes YOLOv6: A Single-Stage Object Detection Framework for Industrial Applications

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-05T12:42:33.512651Z

Source-reported events for the cited work

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Observation 1cb9d110-c0ef-4230-9a17-493561ed7dfd · outbound

This paper cites A comprehensive review of yolo architectures in computer vision: From yolov1 to yolov8 and yolo-nas,.

Image Quality Enhancement and Detection of Small and Dense Objects in Industrial Recycling Processes A comprehensive review of yolo architectures in computer vision: From yolov1 to yolov8 and yolo-nas,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:42:33.684347Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 0ba64e75-f619-42a4-9bf0-5e6e4bc4bf22 · outbound

This paper cites YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information.

Image Quality Enhancement and Detection of Small and Dense Objects in Industrial Recycling Processes YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-05T12:42:33.520636Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bb681c69-9ca3-492f-90f7-d9f928114af8 · outbound

This paper cites YOLOv10: Real-Time End-to-End Object Detection.

Image Quality Enhancement and Detection of Small and Dense Objects in Industrial Recycling Processes YOLOv10: Real-Time End-to-End Object Detection

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-05T12:42:33.524981Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8957a078-03cc-4382-9791-95cdf4545577 · outbound

This paper cites Ultralytics yolo11,.

Image Quality Enhancement and Detection of Small and Dense Objects in Industrial Recycling Processes Ultralytics yolo11,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:42:33.669697Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation faac0b7b-247c-45ce-9891-e1f576aece53 · outbound

This paper cites Detrs beat yolos on real-time object detection,.

Image Quality Enhancement and Detection of Small and Dense Objects in Industrial Recycling Processes Detrs beat yolos on real-time object detection,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:42:33.654658Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 79e6b510-a87a-41d8-acae-3d19cde2a539 · outbound

This paper cites Segment anything,.

Image Quality Enhancement and Detection of Small and Dense Objects in Industrial Recycling Processes Segment anything,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:42:33.640693Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Pith citing papers

Observation 8e14dd49-9e6a-4174-8cd1-b08b7cd38fc7 · inbound