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Paper Citation Record · LEDGER

Taylor expansion-based Kolmogorov-Arnold network for blind image quality assessment

As of 8 August 2026, this Paper Citation Record lists 39 of 39 outbound references and 0 inbound Pith citation observations for arXiv:2505.21592.

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

pith.paper-citation-record.v1
2505.21592 v1

Coverage vector

measured 39 of 39 reference resolution

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Pith citing papers itemized under the disclosed page cap.

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

Source: cited_works

Reference resolution

39 of 39 outbound references displayed

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External citation measurements

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Outbound references

Observation 48e56b45-6dba-469f-9300-41d7afaa376d · outbound

This paper cites Perceptual image quality assessment: a survey,.

Taylor expansion-based Kolmogorov-Arnold network for blind image quality assessment Perceptual image quality assessment: a survey,

Reference 1

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Observation 362d66a7-0105-4bc2-bdb7-722a92343006 · outbound

This paper cites A hybrid indicator for realistic blurred image quality assessment,.

Taylor expansion-based Kolmogorov-Arnold network for blind image quality assessment A hybrid indicator for realistic blurred image quality assessment,

Reference 2

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Observation 0e8f46ea-386c-483d-9dc3-394a000dc25b · outbound

This paper cites Making a ‘completely blind’ image quality analyzer,.

Taylor expansion-based Kolmogorov-Arnold network for blind image quality assessment Making a ‘completely blind’ image quality analyzer,

Reference 3

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Observation 4863c675-5a0f-49d4-9279-fc52b6869778 · outbound

This paper cites A feature-enriched completely blind image quality evaluator,.

Taylor expansion-based Kolmogorov-Arnold network for blind image quality assessment A feature-enriched completely blind image quality evaluator,

Reference 4

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Observation 7a61fa0b-bd3b-46e0-b80f-19f0e8ef4c3c · outbound

This paper cites No-reference image quality assessment in the spatial domain,.

Taylor expansion-based Kolmogorov-Arnold network for blind image quality assessment No-reference image quality assessment in the spatial domain,

Reference 5

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Observation 40d71292-2574-4d9b-8149-c439fd520bb7 · outbound

This paper cites Blind image quality esti- mation via distortion aggravation,.

Taylor expansion-based Kolmogorov-Arnold network for blind image quality assessment Blind image quality esti- mation via distortion aggravation,

Reference 6

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Observation 20e4d237-2c07-4f05-9e1f-d5f5ea4bcc01 · outbound

This paper cites An efficient and effective blind camera image quality metric via modeling quaternion wavelet coefficients,.

Taylor expansion-based Kolmogorov-Arnold network for blind image quality assessment An efficient and effective blind camera image quality metric via modeling quaternion wavelet coefficients,

Reference 7

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Observation 8c781665-95d2-429a-9d5d-3a755b86b1fb · outbound

This paper cites Pseudo light field image and 4D Wavelet-transform-based reduced-reference light field image quality assessment,.

Taylor expansion-based Kolmogorov-Arnold network for blind image quality assessment Pseudo light field image and 4D Wavelet-transform-based reduced-reference light field image quality assessment,

Reference 8

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Observation 911fb4b0-96e7-4ebc-b556-c54907a7e919 · outbound

This paper cites CNN-GRNN for image sharpness assessment,.

Taylor expansion-based Kolmogorov-Arnold network for blind image quality assessment CNN-GRNN for image sharpness assessment,

Reference 9

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Observation af7ce011-13b0-48cd-a747-8b525580eedf · outbound

This paper cites A shallow convolutional neural network for blind image sharpness assessment,.

Taylor expansion-based Kolmogorov-Arnold network for blind image quality assessment A shallow convolutional neural network for blind image sharpness assessment,

Reference 10

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Observation cfd91fcf-7d0d-4139-b3d4-1f1fbcefdc53 · outbound

This paper cites The unreasonable effectiveness of deep features as a perceptual metric,.

Taylor expansion-based Kolmogorov-Arnold network for blind image quality assessment The unreasonable effectiveness of deep features as a perceptual metric,

Reference 11

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Observation d8c57a93-8039-4b2c-945e-02dd7ec91271 · outbound

This paper cites Feature rectification and enhancement for no-reference image quality assessment,.

Taylor expansion-based Kolmogorov-Arnold network for blind image quality assessment Feature rectification and enhancement for no-reference image quality assessment,

Reference 12

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Observation dcdfc720-fc43-4537-b60e-02bc59c79f33 · outbound

This paper cites Deep neural networks for no-reference and full-reference image quality assessment,.

Taylor expansion-based Kolmogorov-Arnold network for blind image quality assessment Deep neural networks for no-reference and full-reference image quality assessment,

Reference 13

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Observation 99d3aba0-0f36-4267-a24c-a70ca02fd005 · outbound

This paper cites Which has better visual quality: The clear blue sky or a blurry animal?.

Taylor expansion-based Kolmogorov-Arnold network for blind image quality assessment Which has better visual quality: The clear blue sky or a blurry animal?

Reference 14

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Observation f262ac98-ec16-4d97-9865-d890a3f19f74 · outbound

This paper cites MetaIQA: Deep meta- learning for no-reference image quality assessment,.

Taylor expansion-based Kolmogorov-Arnold network for blind image quality assessment MetaIQA: Deep meta- learning for no-reference image quality assessment,

Reference 15

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Source-reported events for the cited work

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Observation f0a9e775-666d-4fd3-a792-a80aa85ab185 · outbound

This paper cites Explainable and Generalizable Blind Image Quality Assessment via Semantic Attribute Reasoning,.

Taylor expansion-based Kolmogorov-Arnold network for blind image quality assessment Explainable and Generalizable Blind Image Quality Assessment via Semantic Attribute Reasoning,

Reference 16

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Observation 4d82768f-bd7f-4b4b-beb6-3aaa97314d83 · outbound

This paper cites Blindly assess image quality in the wild guided by a self-adaptive hyper network,.

Taylor expansion-based Kolmogorov-Arnold network for blind image quality assessment Blindly assess image quality in the wild guided by a self-adaptive hyper network,

Reference 17

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Observation dae8a9bf-b33a-4bef-a0dd-49e6639343b8 · outbound

This paper cites Blind image quality assessment using a deep bilinear convolutional neural network,.

Taylor expansion-based Kolmogorov-Arnold network for blind image quality assessment Blind image quality assessment using a deep bilinear convolutional neural network,

Reference 18

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Observation 1020c81c-48eb-47b3-9fb3-7b13907ac85a · outbound

This paper cites Quality-aware pre- trained models for blind image quality assessment,.

Taylor expansion-based Kolmogorov-Arnold network for blind image quality assessment Quality-aware pre- trained models for blind image quality assessment,

Reference 19

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Observation 3c5ead21-26d1-419c-9060-d6d6466445a4 · outbound

This paper cites Blind Image Quality Assessment Based on Geometric Order Learning,.

Taylor expansion-based Kolmogorov-Arnold network for blind image quality assessment Blind Image Quality Assessment Based on Geometric Order Learning,

Reference 20

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Observation f8243b2f-2171-404c-9651-0b98980277ab · outbound

This paper cites A comprehensive study of multimodal large language models for image quality assessment,.

Taylor expansion-based Kolmogorov-Arnold network for blind image quality assessment A comprehensive study of multimodal large language models for image quality assessment,

Reference 21

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Observation df011683-6d28-4461-be06-7c7f6de8ba5a · outbound

This paper cites Blind image quality assessment via vision-language correspondence: A multitask learning perspective,.

Taylor expansion-based Kolmogorov-Arnold network for blind image quality assessment Blind image quality assessment via vision-language correspondence: A multitask learning perspective,

Reference 22

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Observation 07aed09d-6aac-4610-b498-6e218670a7ef · outbound

This paper cites KAN: Kolmogorov-Arnold Networks.

Taylor expansion-based Kolmogorov-Arnold network for blind image quality assessment KAN: Kolmogorov-Arnold Networks

Reference 23

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Observation fb5bda85-6dc9-4014-9301-120ca3104fc2 · outbound

This paper cites Kolmogorov-Arnold Networks: A Critical Assessment of Claims, Performance, and Practical Viability.

Taylor expansion-based Kolmogorov-Arnold network for blind image quality assessment Kolmogorov-Arnold Networks: A Critical Assessment of Claims, Performance, and Practical Viability

Reference 24

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Observation f73899db-31d6-434a-a190-050629641dbe · outbound

This paper cites Exploring Kolmogorov-Arnold networks for realistic image sharpness assessment,.

Taylor expansion-based Kolmogorov-Arnold network for blind image quality assessment Exploring Kolmogorov-Arnold networks for realistic image sharpness assessment,

Reference 25

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Observation 00b0ef6a-76b0-41c3-9a54-319fbfd36b81 · outbound

This paper cites No-reference blur assessment of digital pictures based on multifeature classifiers,.

Taylor expansion-based Kolmogorov-Arnold network for blind image quality assessment No-reference blur assessment of digital pictures based on multifeature classifiers,

Reference 26

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Observation 865f7ef5-1d5b-4e60-a667-8f5b8eceb4a9 · outbound

This paper cites Massive online crowdsourced study of subjective and objective picture quality,.

Taylor expansion-based Kolmogorov-Arnold network for blind image quality assessment Massive online crowdsourced study of subjective and objective picture quality,

Reference 27

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Observation e4468443-d292-4db7-aa58-a31de574e473 · outbound

This paper cites KonIQ-10k: An ecologi- cally valid database for deep learning of blind image quality assessment,.

Taylor expansion-based Kolmogorov-Arnold network for blind image quality assessment KonIQ-10k: An ecologi- cally valid database for deep learning of blind image quality assessment,

Reference 28

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Observation 54881f32-a692-42ac-a836-4550cb5bee9d · outbound

This paper cites Perceptual quality assessment of smartphone photography,.

Taylor expansion-based Kolmogorov-Arnold network for blind image quality assessment Perceptual quality assessment of smartphone photography,

Reference 29

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Observation 70203476-ca41-4682-99e9-cca1bf4da9c3 · outbound

This paper cites From patches to pictures (PaQ-2-PiQ): Mapping the perceptual space of picture quality,.

Taylor expansion-based Kolmogorov-Arnold network for blind image quality assessment From patches to pictures (PaQ-2-PiQ): Mapping the perceptual space of picture quality,

Reference 30

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Source-reported events for the cited work

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Observation 36ef3108-6564-41b1-9205-00d40abaefe0 · outbound

This paper cites Deep residual learning for image recognition,.

Taylor expansion-based Kolmogorov-Arnold network for blind image quality assessment Deep residual learning for image recognition,

Reference 31

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Observation ab462a2e-7fd8-41d6-82ad-7fa0aff4ce62 · outbound

This paper cites Chebyshev Polynomial-Based Kolmogorov-Arnold Networks: An Efficient Architecture for Nonlinear Function Approximation.

Taylor expansion-based Kolmogorov-Arnold network for blind image quality assessment Chebyshev Polynomial-Based Kolmogorov-Arnold Networks: An Efficient Architecture for Nonlinear Function Approximation

Reference 32

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Observation 169c9e2f-a6e5-4521-9f45-cb458098d7d5 · outbound

This paper cites fKAN: Fractional Kolmogorov-Arnold Networks with trainable Jacobi basis functions.

Taylor expansion-based Kolmogorov-Arnold network for blind image quality assessment fKAN: Fractional Kolmogorov-Arnold Networks with trainable Jacobi basis functions

Reference 33

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Observation 79765ddf-5b13-4b4f-9cac-e8faa0eea4f8 · outbound

This paper cites Wav-KAN: Wavelet Kolmogorov-Arnold Networks.

Taylor expansion-based Kolmogorov-Arnold network for blind image quality assessment Wav-KAN: Wavelet Kolmogorov-Arnold Networks

Reference 34

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Source-reported events for the cited work

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Observation b6a076b2-daa9-4b25-aa35-44154ebb3cea · outbound

This paper cites Enhancing Graph Collaborative Filtering with FourierKAN Feature Transformation.

Taylor expansion-based Kolmogorov-Arnold network for blind image quality assessment Enhancing Graph Collaborative Filtering with FourierKAN Feature Transformation

Reference 35

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source=pdf_text observed=2026-08-07T13:43:15.777531Z digest=sha256:985df3d46f23e899685e8263678d14871859b7d15718d032f9479b39b1d48110

Observation a386207b-3e45-409f-8c1e-e4c68e9af1a6 · outbound

This paper cites BSRBF-KAN: A combination of B-splines and Radial Basis Functions in Kolmogorov-Arnold Networks.

Taylor expansion-based Kolmogorov-Arnold network for blind image quality assessment BSRBF-KAN: A combination of B-splines and Radial Basis Functions in Kolmogorov-Arnold Networks

Reference 36

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Observation 21a511b9-1470-4184-9929-bc836a19875b · outbound

This paper cites Kolmogorov-Arnold Networks are Radial Basis Function Networks.

Taylor expansion-based Kolmogorov-Arnold network for blind image quality assessment Kolmogorov-Arnold Networks are Radial Basis Function Networks

Reference 37

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Observation 562d3d7c-e3ab-4c3a-bd86-7157c36478ea · outbound

This paper cites matFR: a MATLAB toolbox for feature ranking,.

Taylor expansion-based Kolmogorov-Arnold network for blind image quality assessment matFR: a MATLAB toolbox for feature ranking,

Reference 38

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source=pdf_text observed=2026-08-07T13:43:16.058309Z digest=sha256:9d10a8db18f157173a371647bd35e80775c4cf590cdb3a997b79b3094f7dba93

Observation 24ced4f4-bc24-4353-8444-90230017cc84 · outbound

This paper cites KAN 2.0: Kolmogorov-Arnold Networks Meet Science.

Taylor expansion-based Kolmogorov-Arnold network for blind image quality assessment KAN 2.0: Kolmogorov-Arnold Networks Meet Science

Reference 39

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Pith citing papers

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