Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Underwater Image Enhancement based on Deep Learning and Image Formation Model
As of 22 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2101.00991.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-16T04:11:32.820627Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-01T14:45:49.661271Z
Reference resolution
0 of 0 outbound references displayed
- verified exact0
- verified fuzzy0
- unresolved0
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
No outbound reference observations are available for this paper version.
Pith citing papers
Observation ef2a1550-ff62-4e33-ace6-5f2e39e06039 · inbound
HUPE: Heuristic Underwater Perceptual Enhancement with Semantic Collaborative Learning cites this paper.
HUPE: Heuristic Underwater Perceptual Enhancement with Semantic Collaborative Learning Underwater Image Enhancement based on Deep Learning and Image Formation Model
Reference 28
- Resolution
- unresolved
- no resolver link, observed 2026-08-12T11:24:30.684018Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 6f7195d2-20b4-4ebc-84f6-f9f9cf241948 · inbound
Visual enhancement and 3D representation for underwater scenes: a review cites this paper.
Visual enhancement and 3D representation for underwater scenes: a review Underwater Image Enhancement based on Deep Learning and Image Formation Model
Reference 29
- Resolution
- unresolved
- no resolver link, observed 2026-08-16T04:11:32.820627Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 6a28088e-f12d-411b-b62c-beb5da42229d · inbound
Excavate the potential of Single-Scale Features: A Decomposition Network for Water-Related Optical Image Enhancement cites this paper.
Excavate the potential of Single-Scale Features: A Decomposition Network for Water-Related Optical Image Enhancement Underwater Image Enhancement based on Deep Learning and Image Formation Model
Reference 17
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T00:56:22.746337Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 29e8e18f-edf2-4671-9db5-0ec73bc3cf37 · inbound
An Underwater Dehazing Network with Implicit Transmission Estimation cites this paper.
An Underwater Dehazing Network with Implicit Transmission Estimation Underwater Image Enhancement based on Deep Learning and Image Formation Model
Reference 22
- Resolution
- verified exact
- arxiv_id, observed 2026-05-14T17:42:31.258844Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 52387212-a116-459a-aaea-36e737069ff4 · inbound
An Attention-Enhanced Network with Joint Dehazing and Retinex-Based Enhancement for Underwater Images cites this paper.
An Attention-Enhanced Network with Joint Dehazing and Retinex-Based Enhancement for Underwater Images Underwater Image Enhancement based on Deep Learning and Image Formation Model
Reference 25
- Resolution
- verified exact
- arxiv_id, observed 2026-07-01T14:45:49.662714Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.