ISALux: Illumination and Segmentation Aware Transformer Employing Mixture of Experts for Low Light Image Enhancement | Paper Citation Record

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This paper cites write newline.

ISALux: Illumination and Segmentation Aware Transformer Employing Mixture of Experts for Low Light Image Enhancement write newline

Reference 1

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This paper cites an unresolved cited work.

ISALux: Illumination and Segmentation Aware Transformer Employing Mixture of Experts for Low Light Image Enhancement Unresolved cited work

Reference 2

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This paper cites Low-light image and video enhancement using deep learning: A survey.

ISALux: Illumination and Segmentation Aware Transformer Employing Mixture of Experts for Low Light Image Enhancement Low-light image and video enhancement using deep learning: A survey

Reference 3

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This paper cites an unresolved cited work.

ISALux: Illumination and Segmentation Aware Transformer Employing Mixture of Experts for Low Light Image Enhancement Unresolved cited work

Reference 4

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Observation 8ef61990-e4d3-40dc-8c47-02511d5c445f · outbound

This paper cites Moran, P.

ISALux: Illumination and Segmentation Aware Transformer Employing Mixture of Experts for Low Light Image Enhancement Moran, P

Reference 5

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This paper cites an unresolved cited work.

ISALux: Illumination and Segmentation Aware Transformer Employing Mixture of Experts for Low Light Image Enhancement Unresolved cited work

Reference 6

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This paper cites Deep retinex decomposition for low-light enhancement.

ISALux: Illumination and Segmentation Aware Transformer Employing Mixture of Experts for Low Light Image Enhancement Deep retinex decomposition for low-light enhancement

Reference 7

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This paper cites Attention is all you need.

ISALux: Illumination and Segmentation Aware Transformer Employing Mixture of Experts for Low Light Image Enhancement Attention is all you need

Reference 8

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This paper cites Lora: Low-rank adaptation of large language models.

ISALux: Illumination and Segmentation Aware Transformer Employing Mixture of Experts for Low Light Image Enhancement Lora: Low-rank adaptation of large language models

Reference 9

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This paper cites Kindling the darkness: A practical low-light image enhancer.

ISALux: Illumination and Segmentation Aware Transformer Employing Mixture of Experts for Low Light Image Enhancement Kindling the darkness: A practical low-light image enhancer

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:02:53.379668Z

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This paper cites Zhang, Y.

ISALux: Illumination and Segmentation Aware Transformer Employing Mixture of Experts for Low Light Image Enhancement Zhang, Y

Reference 11

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This paper cites an unresolved cited work.

ISALux: Illumination and Segmentation Aware Transformer Employing Mixture of Experts for Low Light Image Enhancement Unresolved cited work

Reference 12

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This paper cites An image is worth 16x16 words: Transformers for image recognition at scale.

ISALux: Illumination and Segmentation Aware Transformer Employing Mixture of Experts for Low Light Image Enhancement An image is worth 16x16 words: Transformers for image recognition at scale

Reference 13

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This paper cites Tokens-to-token vit: Training vision transformers from scratch on imagenet.

ISALux: Illumination and Segmentation Aware Transformer Employing Mixture of Experts for Low Light Image Enhancement Tokens-to-token vit: Training vision transformers from scratch on imagenet

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:02:53.305203Z

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This paper cites Pyramid vision transformer: A versatile backbone for dense prediction without convolutions.

ISALux: Illumination and Segmentation Aware Transformer Employing Mixture of Experts for Low Light Image Enhancement Pyramid vision transformer: A versatile backbone for dense prediction without convolutions

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:02:53.288077Z

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This paper cites Zheng, J.

ISALux: Illumination and Segmentation Aware Transformer Employing Mixture of Experts for Low Light Image Enhancement Zheng, J

Reference 16

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Observation d335bd98-8482-4b77-968a-d40dafd39f03 · outbound

This paper cites Hierarchical vision transformer using shifted windows.

ISALux: Illumination and Segmentation Aware Transformer Employing Mixture of Experts for Low Light Image Enhancement Hierarchical vision transformer using shifted windows

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:02:53.251095Z

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This paper cites an unresolved cited work.

ISALux: Illumination and Segmentation Aware Transformer Employing Mixture of Experts for Low Light Image Enhancement Unresolved cited work

Reference 18

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Observation 3564c0e1-99aa-406a-a08b-f938b091c583 · outbound

This paper cites Learning texture transformer network for image super-resolution.

ISALux: Illumination and Segmentation Aware Transformer Employing Mixture of Experts for Low Light Image Enhancement Learning texture transformer network for image super-resolution

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:02:53.209606Z

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This paper cites Activating more pixels in image super-resolution transformer.

ISALux: Illumination and Segmentation Aware Transformer Employing Mixture of Experts for Low Light Image Enhancement Activating more pixels in image super-resolution transformer

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:02:53.183749Z

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This paper cites Vision transformers for single image dehazing.

ISALux: Illumination and Segmentation Aware Transformer Employing Mixture of Experts for Low Light Image Enhancement Vision transformers for single image dehazing

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:02:53.157940Z

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This paper cites Guibas, Dilip Krishnan, Kilian Q Weinberger, Yonglong Tian, and Yue Wang.

ISALux: Illumination and Segmentation Aware Transformer Employing Mixture of Experts for Low Light Image Enhancement Guibas, Dilip Krishnan, Kilian Q Weinberger, Yonglong Tian, and Yue Wang

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:02:53.126319Z

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This paper cites an unresolved cited work.

ISALux: Illumination and Segmentation Aware Transformer Employing Mixture of Experts for Low Light Image Enhancement Unresolved cited work

Reference 23

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Observation 2f57ab4a-1a49-4224-8bbb-1c6a2c28117c · outbound

This paper cites Zhang, Y.

ISALux: Illumination and Segmentation Aware Transformer Employing Mixture of Experts for Low Light Image Enhancement Zhang, Y

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:02:53.081381Z

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This paper cites an unresolved cited work.

ISALux: Illumination and Segmentation Aware Transformer Employing Mixture of Experts for Low Light Image Enhancement Unresolved cited work

Reference 25

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Observation c5546984-f47a-499e-8ff3-2c041d95d660 · outbound

This paper cites Retinexformer: One-stage retinex-based transformer for low-light image enhancement.

ISALux: Illumination and Segmentation Aware Transformer Employing Mixture of Experts for Low Light Image Enhancement Retinexformer: One-stage retinex-based transformer for low-light image enhancement

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:02:53.018314Z

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Observation 7f03f688-4c5a-4cf6-a17a-1da72d94b34d · outbound

This paper cites Glam: Efficient scaling of language models with mixture-of-experts.

ISALux: Illumination and Segmentation Aware Transformer Employing Mixture of Experts for Low Light Image Enhancement Glam: Efficient scaling of language models with mixture-of-experts

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:02:52.993747Z

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Observation e9107a39-0bdb-4de1-8e8a-22bbc854b5cf · outbound

This paper cites Raphael: Text-to-image generation via large mixture of diffusion paths.

ISALux: Illumination and Segmentation Aware Transformer Employing Mixture of Experts for Low Light Image Enhancement Raphael: Text-to-image generation via large mixture of diffusion paths

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:02:52.963167Z

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Observation 6c575f16-cc0d-4779-9a70-9ae15fc3e35c · outbound

This paper cites Cumo: Scaling multimodal llm with co-upcycled mixture-of-experts.

ISALux: Illumination and Segmentation Aware Transformer Employing Mixture of Experts for Low Light Image Enhancement Cumo: Scaling multimodal llm with co-upcycled mixture-of-experts

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:02:52.938116Z

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This paper cites Self-moe: Towards compositional large language models with self-specialized experts, 2024.

ISALux: Illumination and Segmentation Aware Transformer Employing Mixture of Experts for Low Light Image Enhancement Self-moe: Towards compositional large language models with self-specialized experts, 2024

Reference 30

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Observation b5f80af6-690e-4d2a-8bc0-99c1a6d21099 · outbound

This paper cites Towards understanding the mixture-of-experts layer in deep learning.

ISALux: Illumination and Segmentation Aware Transformer Employing Mixture of Experts for Low Light Image Enhancement Towards understanding the mixture-of-experts layer in deep learning

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:02:52.897376Z

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This paper cites Patch-level routing in mixture-of-experts is provably sample-efficient for convolutional neural networks.

ISALux: Illumination and Segmentation Aware Transformer Employing Mixture of Experts for Low Light Image Enhancement Patch-level routing in mixture-of-experts is provably sample-efficient for convolutional neural networks

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:02:52.876451Z

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Observation 74062c3b-00ee-415b-9060-21a5dbfb3af2 · outbound

This paper cites Adamv-moe: Adaptive multi-task vision mixture-of-experts.

ISALux: Illumination and Segmentation Aware Transformer Employing Mixture of Experts for Low Light Image Enhancement Adamv-moe: Adaptive multi-task vision mixture-of-experts

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:02:52.847746Z

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This paper cites Ace: Ally complementary experts for solving long-tailed recognition in one-shot.

ISALux: Illumination and Segmentation Aware Transformer Employing Mixture of Experts for Low Light Image Enhancement Ace: Ally complementary experts for solving long-tailed recognition in one-shot

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:02:52.820639Z

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Observation e4b96f2b-c2f8-4aaf-a60d-6b3e684bafe3 · outbound

This paper cites Blind single image super-resolution with a mixture of deep networks.

ISALux: Illumination and Segmentation Aware Transformer Employing Mixture of Experts for Low Light Image Enhancement Blind single image super-resolution with a mixture of deep networks

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:02:52.792112Z

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Observation 72b6bb83-8417-44c7-9b4f-704d2f7d1f6b · outbound

This paper cites Moesr: Blind super-resolution using kernel-aware mixture of experts.

ISALux: Illumination and Segmentation Aware Transformer Employing Mixture of Experts for Low Light Image Enhancement Moesr: Blind super-resolution using kernel-aware mixture of experts

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:02:52.765772Z

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This paper cites Parameter efficient adaptation for image restoration with heterogeneous mixture-of-experts.

ISALux: Illumination and Segmentation Aware Transformer Employing Mixture of Experts for Low Light Image Enhancement Parameter efficient adaptation for image restoration with heterogeneous mixture-of-experts

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:02:52.737890Z

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Observation 98d77be8-574f-498f-a0f3-5a4f4b8aecea · outbound

This paper cites Rethinking Atrous Convolution for Semantic Image Segmentation.

ISALux: Illumination and Segmentation Aware Transformer Employing Mixture of Experts for Low Light Image Enhancement Rethinking Atrous Convolution for Semantic Image Segmentation

Reference 38

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This paper cites Microsoft coco: Common objects in context.

ISALux: Illumination and Segmentation Aware Transformer Employing Mixture of Experts for Low Light Image Enhancement Microsoft coco: Common objects in context

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:02:52.712056Z

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This paper cites Jacobs, Michael I.

ISALux: Illumination and Segmentation Aware Transformer Employing Mixture of Experts for Low Light Image Enhancement Jacobs, Michael I

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:02:52.688170Z

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Observation b53d2499-3aa4-49f7-9f9a-bc2ccfbcb1cd · outbound

This paper cites Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer.

ISALux: Illumination and Segmentation Aware Transformer Employing Mixture of Experts for Low Light Image Enhancement Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer

Reference 41

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ISALux: Illumination and Segmentation Aware Transformer Employing Mixture of Experts for Low Light Image Enhancement Unresolved cited work

Reference 42

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This paper cites an unresolved cited work.

ISALux: Illumination and Segmentation Aware Transformer Employing Mixture of Experts for Low Light Image Enhancement Unresolved cited work

Reference 43

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Observation a5567e7f-2c88-4118-8e97-e37d89598245 · outbound

This paper cites an unresolved cited work.

ISALux: Illumination and Segmentation Aware Transformer Employing Mixture of Experts for Low Light Image Enhancement Unresolved cited work

Reference 44

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Observation 02e3d8b8-db2a-4744-ad02-32e747a86092 · outbound

This paper cites an unresolved cited work.

ISALux: Illumination and Segmentation Aware Transformer Employing Mixture of Experts for Low Light Image Enhancement Unresolved cited work

Reference 45

Resolution: unresolved; raw_fallback, observed 2026-08-15T17:02:52.583999Z

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Observation dbceeeca-4557-4a30-b914-2ebe1d4ce020 · outbound

This paper cites Very deep convolutional networks for large-scale image recognition.

ISALux: Illumination and Segmentation Aware Transformer Employing Mixture of Experts for Low Light Image Enhancement Very deep convolutional networks for large-scale image recognition

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:02:52.559110Z

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Observation 97c82b6f-fe0a-4935-8116-99349fea6685 · outbound

This paper cites Sparse gradient regularized deep retinex network for robust low-light image enhancement.

ISALux: Illumination and Segmentation Aware Transformer Employing Mixture of Experts for Low Light Image Enhancement Sparse gradient regularized deep retinex network for robust low-light image enhancement

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:02:52.536571Z

Source-reported events for the cited work

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Observation 063282a0-a006-4bf1-a01c-be2710da3ead · outbound

This paper cites an unresolved cited work.

ISALux: Illumination and Segmentation Aware Transformer Employing Mixture of Experts for Low Light Image Enhancement Unresolved cited work

Reference 48

Resolution: unresolved; raw_fallback, observed 2026-08-15T17:02:52.512826Z

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Observation 18d0f9e8-a594-4256-a525-d01ab104dee5 · outbound

This paper cites an unresolved cited work.

ISALux: Illumination and Segmentation Aware Transformer Employing Mixture of Experts for Low Light Image Enhancement Unresolved cited work

Reference 49

Resolution: unresolved; raw_fallback, observed 2026-08-15T17:02:52.492013Z

Source-reported events for the cited work

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Observation 0a8d21b1-eaed-407a-9228-519af9859114 · outbound

This paper cites Lime: Low-light image enhancement via illumination map estimation.

ISALux: Illumination and Segmentation Aware Transformer Employing Mixture of Experts for Low Light Image Enhancement Lime: Low-light image enhancement via illumination map estimation

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:02:52.468625Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation e478066a-4553-45ab-b4bf-c692ab5c01b4 · outbound

This paper cites Naturalness preserved enhancement algorithm for non-uniform illumination images.

ISALux: Illumination and Segmentation Aware Transformer Employing Mixture of Experts for Low Light Image Enhancement Naturalness preserved enhancement algorithm for non-uniform illumination images

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:02:52.448636Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 31645ec5-b9b5-4cf3-bad0-693589d36088 · outbound

This paper cites Perceptual quality assessment for multi-exposure image fusion.

ISALux: Illumination and Segmentation Aware Transformer Employing Mixture of Experts for Low Light Image Enhancement Perceptual quality assessment for multi-exposure image fusion

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:02:52.429394Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 9f2f5a32-dfbf-46f9-88f5-65f5f5981f0f · outbound

This paper cites Contrast enhancement based on layered difference representation of 2d histograms.

ISALux: Illumination and Segmentation Aware Transformer Employing Mixture of Experts for Low Light Image Enhancement Contrast enhancement based on layered difference representation of 2d histograms

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:02:52.409246Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 75ee59d8-329e-40be-b32f-da0050777b36 · outbound

This paper cites completely blind.

ISALux: Illumination and Segmentation Aware Transformer Employing Mixture of Experts for Low Light Image Enhancement completely blind

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:02:52.387255Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation d08a299a-1ee2-4e14-bb90-acce19c669e1 · outbound

This paper cites Deblurgan-v2: Deblurring (orders-of-magnitude) faster and better.

ISALux: Illumination and Segmentation Aware Transformer Employing Mixture of Experts for Low Light Image Enhancement Deblurgan-v2: Deblurring (orders-of-magnitude) faster and better

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:02:52.368006Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation cbeeaece-ad3d-4545-bdc1-c99b368243f9 · outbound

This paper cites Learning semantic-aware knowledge guidance for low-light image enhancement.

ISALux: Illumination and Segmentation Aware Transformer Employing Mixture of Experts for Low Light Image Enhancement Learning semantic-aware knowledge guidance for low-light image enhancement

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:02:52.347658Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 256f63b9-9172-41f0-91be-1f6e93214a55 · outbound

This paper cites You do not need additional priors or regularizers in retinex-based low-light image enhancement.

ISALux: Illumination and Segmentation Aware Transformer Employing Mixture of Experts for Low Light Image Enhancement You do not need additional priors or regularizers in retinex-based low-light image enhancement

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:02:52.317941Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 4b77ff73-cf38-49b4-8d1e-72a6b7c27211 · outbound

This paper cites Asp-led: Learning ambiguity-aware structural priors for joint low-light enhancement and deblurring.

ISALux: Illumination and Segmentation Aware Transformer Employing Mixture of Experts for Low Light Image Enhancement Asp-led: Learning ambiguity-aware structural priors for joint low-light enhancement and deblurring

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:02:52.293546Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 576a3056-24e9-448c-bc44-127505d7f207 · outbound

This paper cites VQCNIR: Clearer Night Image Restoration with Vector-Quantized Codebook.

ISALux: Illumination and Segmentation Aware Transformer Employing Mixture of Experts for Low Light Image Enhancement VQCNIR: Clearer Night Image Restoration with Vector-Quantized Codebook

Reference 59

Resolution: verified exact; local_arxiv, observed 2026-08-15T17:02:52.211477Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

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