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Paper Citation Record · LEDGER

GMODiff: One-Step Gain Map Refinement with Diffusion Priors for HDR Reconstruction

As of 19 August 2026, this Paper Citation Record lists 46 of 46 outbound references and 1 inbound Pith citation observation for arXiv:2512.16357.

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

pith.paper-citation-record.v1
2512.16357 v3

Coverage vector

measured 46 of 46 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-03T15:36:28.077648Z

measured 47 of 47 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 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T01:10:31.308513Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-07T01:10:35.484880Z

Reference resolution

46 of 46 outbound references displayed

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External citation measurements

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Outbound references

Observation 19627e4c-509f-4fad-8c70-3e004aa89bab · outbound

This paper cites Gain map specification.https : / / helpx.

GMODiff: One-Step Gain Map Refinement with Diffusion Priors for HDR Reconstruction Gain map specification.https : / / helpx

Reference 1

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Observation a4d352e5-3e66-4451-a6f5-f571b46c4a50 · outbound

This paper cites Explore hdr rendering with edr.https : / / developer.

GMODiff: One-Step Gain Map Refinement with Diffusion Priors for HDR Reconstruction Explore hdr rendering with edr.https : / / developer

Reference 2

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Observation 5cadcdba-02a5-42af-85c6-ae8ad302eaee · outbound

This paper cites Pu21: A novel perceptually uniform encoding for adapting existing quality metrics for hdr.

GMODiff: One-Step Gain Map Refinement with Diffusion Priors for HDR Reconstruction Pu21: A novel perceptually uniform encoding for adapting existing quality metrics for hdr

Reference 3

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Observation 1fd575ed-416e-4748-b756-ab592d37eb95 · outbound

This paper cites Extending dynamic range of monochrome and color images through fusion.

GMODiff: One-Step Gain Map Refinement with Diffusion Priors for HDR Reconstruction Extending dynamic range of monochrome and color images through fusion

Reference 4

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source=pdf_text observed=2026-08-03T15:36:22.225712Z digest=sha256:f60c33a27fa90669ca1292141fa720994ac199fbd2d5b97defa14f030fe2f99f

Observation c6226bb2-b8bb-4a57-b353-0d69320ddbb7 · outbound

This paper cites Adversarial diffusion compression for real-world image super-resolution.

GMODiff: One-Step Gain Map Refinement with Diffusion Priors for HDR Reconstruction Adversarial diffusion compression for real-world image super-resolution

Reference 5

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Observation 90a54ca8-fe10-4121-b71e-9a6325f60587 · outbound

This paper cites Attention-guided progressive neural tex- ture fusion for high dynamic range image restoration.IEEE Transactions on Image Processing, 31:2661–2672, 2022.

GMODiff: One-Step Gain Map Refinement with Diffusion Priors for HDR Reconstruction Attention-guided progressive neural tex- ture fusion for high dynamic range image restoration.IEEE Transactions on Image Processing, 31:2661–2672, 2022

Reference 6

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Observation 99696e3b-ecaf-4419-8b10-1b5af4a237e7 · outbound

This paper cites Simple baselines for image restoration.

GMODiff: One-Step Gain Map Refinement with Diffusion Priors for HDR Reconstruction Simple baselines for image restoration

Reference 7

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Observation 8ecdc182-ad54-4f53-84bc-66ff186f4636 · outbound

This paper cites Ultrafusion: Ul- tra high dynamic imaging using exposure fusion.

GMODiff: One-Step Gain Map Refinement with Diffusion Priors for HDR Reconstruction Ultrafusion: Ul- tra high dynamic imaging using exposure fusion

Reference 8

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Observation a2482a93-3680-4991-bb39-1258126000ec · outbound

This paper cites Diffusion models beat gans on image synthesis.NeurIPS, 2021.

GMODiff: One-Step Gain Map Refinement with Diffusion Priors for HDR Reconstruction Diffusion models beat gans on image synthesis.NeurIPS, 2021

Reference 9

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Observation 41dac717-3885-4e05-bcdb-f3b9ab6b72e4 · outbound

This paper cites Superpc: a single diffusion model for point cloud completion, upsampling, de- noising, and colorization.

GMODiff: One-Step Gain Map Refinement with Diffusion Priors for HDR Reconstruction Superpc: a single diffusion model for point cloud completion, upsampling, de- noising, and colorization

Reference 10

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Observation ee771997-d0ed-4063-93b8-edce48eb90b0 · outbound

This paper cites Artifact-free high dynamic range imag- ing.

GMODiff: One-Step Gain Map Refinement with Diffusion Priors for HDR Reconstruction Artifact-free high dynamic range imag- ing

Reference 11

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Observation ef9c3c50-0317-407b-acbd-a724e9a20ff1 · outbound

This paper cites Ultra hdr image format.https://developer.

GMODiff: One-Step Gain Map Refinement with Diffusion Priors for HDR Reconstruction Ultra hdr image format.https://developer

Reference 12

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Observation 231e5f67-3890-40a5-86ad-1e95153f9f7d · outbound

This paper cites Hdr image generation via gain map decomposed diffusion.

GMODiff: One-Step Gain Map Refinement with Diffusion Priors for HDR Reconstruction Hdr image generation via gain map decomposed diffusion

Reference 13

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Observation 6d3313f8-bc05-4239-be18-53548f844782 · outbound

This paper cites Denoising diffu- sion probabilistic models.NeurIPS, 2020.

GMODiff: One-Step Gain Map Refinement with Diffusion Priors for HDR Reconstruction Denoising diffu- sion probabilistic models.NeurIPS, 2020

Reference 14

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Observation 54efdac8-08de-49e7-bbba-de662a752d7f · outbound

This paper cites Lora: Low-rank adaptation of large language models.ICLR, 1(2):3, 2022.

GMODiff: One-Step Gain Map Refinement with Diffusion Priors for HDR Reconstruction Lora: Low-rank adaptation of large language models.ICLR, 1(2):3, 2022

Reference 15

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Observation c9a4498a-d575-4fff-a426-66390cc897c0 · outbound

This paper cites Gallo, K.

GMODiff: One-Step Gain Map Refinement with Diffusion Priors for HDR Reconstruction Gallo, K

Reference 16

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Observation cf7c88a9-bb94-406a-9806-1a02205e4e43 · outbound

This paper cites Gen- erating content for hdr deghosting from frequency view.

GMODiff: One-Step Gain Map Refinement with Diffusion Priors for HDR Reconstruction Gen- erating content for hdr deghosting from frequency view

Reference 17

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Observation 644274c1-3c2a-4030-ae31-6fcd75e9711c · outbound

This paper cites Deep high dynamic range imaging of dynamic scenes.ACM Trans.

GMODiff: One-Step Gain Map Refinement with Diffusion Priors for HDR Reconstruction Deep high dynamic range imaging of dynamic scenes.ACM Trans

Reference 18

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Observation eaf6a1e5-6430-4d53-8c6d-c9e6b3b64e1d · outbound

This paper cites High dynamic range video.ACM Transac- tions on Graphics (TOG), 22(3):319–325, 2003.

GMODiff: One-Step Gain Map Refinement with Diffusion Priors for HDR Reconstruction High dynamic range video.ACM Transac- tions on Graphics (TOG), 22(3):319–325, 2003

Reference 19

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Observation cd2286dd-e2db-414e-a411-ffd6c0a1b226 · outbound

This paper cites Safnet: Selective alignment fusion net- work for efficient hdr imaging.

GMODiff: One-Step Gain Map Refinement with Diffusion Priors for HDR Reconstruction Safnet: Selective alignment fusion net- work for efficient hdr imaging

Reference 20

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Observation 9272dc47-3dce-43a1-8a6b-1bbe70daacb4 · outbound

This paper cites Efficient diffusion as low light enhancer.

GMODiff: One-Step Gain Map Refinement with Diffusion Priors for HDR Reconstruction Efficient diffusion as low light enhancer

Reference 21

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Observation b4179280-924c-42c3-b43e-25a0349b8ac1 · outbound

This paper cites AFUNet: Cross-Iterative Alignment-Fusion Synergy for HDR Reconstruction via Deep Unfolding Paradigm.

GMODiff: One-Step Gain Map Refinement with Diffusion Priors for HDR Reconstruction AFUNet: Cross-Iterative Alignment-Fusion Synergy for HDR Reconstruction via Deep Unfolding Paradigm

Reference 22

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Observation 6ad573b2-ac50-4949-aa68-a7575283ddd5 · outbound

This paper cites Learning gain map for inverse tone mapping.

GMODiff: One-Step Gain Map Refinement with Diffusion Priors for HDR Reconstruction Learning gain map for inverse tone mapping

Reference 23

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Observation dcf241bb-3ad7-41a9-aa90-22894600430a · outbound

This paper cites Ghost-free high dynamic range imaging with context-aware transformer.

GMODiff: One-Step Gain Map Refinement with Diffusion Priors for HDR Reconstruction Ghost-free high dynamic range imaging with context-aware transformer

Reference 24

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Observation 15b033b1-d2fa-4317-aa8d-c512a3020641 · outbound

This paper cites Dpm-solver: A fast ode solver for diffu- sion probabilistic model sampling in around 10 steps.Ad- vances in neural information processing systems, 35:5775– 5787, 2022.

GMODiff: One-Step Gain Map Refinement with Diffusion Priors for HDR Reconstruction Dpm-solver: A fast ode solver for diffu- sion probabilistic model sampling in around 10 steps.Ad- vances in neural information processing systems, 35:5775– 5787, 2022

Reference 25

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Observation 730aed55-f7a5-4162-8265-666df04aaf7d · outbound

This paper cites Semantic masking with curriculum learning for robust hdr image reconstruction: Z.

GMODiff: One-Step Gain Map Refinement with Diffusion Priors for HDR Reconstruction Semantic masking with curriculum learning for robust hdr image reconstruction: Z

Reference 26

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Observation 69a4dbcd-f53e-435e-8304-790f4565675b · outbound

This paper cites Hdr-gan: Hdr image reconstruction from multi-exposed ldr images with large motions.IEEE Trans- actions on Image Processing, 30:3885–3896, 2021.

GMODiff: One-Step Gain Map Refinement with Diffusion Priors for HDR Reconstruction Hdr-gan: Hdr image reconstruction from multi-exposed ldr images with large motions.IEEE Trans- actions on Image Processing, 30:3885–3896, 2021

Reference 27

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Observation f72e1925-1a03-4c4e-b37d-fad7e1226a30 · outbound

This paper cites One-Step Image Translation with Text-to-Image Models.

GMODiff: One-Step Gain Map Refinement with Diffusion Priors for HDR Reconstruction One-Step Image Translation with Text-to-Image Models

Reference 28

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Observation bec645f6-a6e1-4853-9e48-9c4785e5bc0c · outbound

This paper cites SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis.

GMODiff: One-Step Gain Map Refinement with Diffusion Priors for HDR Reconstruction SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis

Reference 29

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Observation f7f626bf-9263-44cb-87a2-7314b9d80093 · outbound

This paper cites High-resolution image synthesis with latent diffusion models.

GMODiff: One-Step Gain Map Refinement with Diffusion Priors for HDR Reconstruction High-resolution image synthesis with latent diffusion models

Reference 30

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Observation d83f6ffe-5096-404b-bf22-1849dc0cc0c5 · outbound

This paper cites Photorealistic text-to-image diffusion models with deep language understanding.NeurIPS, 2022.

GMODiff: One-Step Gain Map Refinement with Diffusion Priors for HDR Reconstruction Photorealistic text-to-image diffusion models with deep language understanding.NeurIPS, 2022

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Observation 00b6296c-ea65-40d9-b16e-524947faeea0 · outbound

This paper cites Ro- bust patch-based hdr reconstruction of dynamic scenes.ACM Trans.

GMODiff: One-Step Gain Map Refinement with Diffusion Priors for HDR Reconstruction Ro- bust patch-based hdr reconstruction of dynamic scenes.ACM Trans

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Observation 2247971d-3450-44d9-bda7-741ea469282b · outbound

This paper cites Denoising Diffusion Implicit Models.

GMODiff: One-Step Gain Map Refinement with Diffusion Priors for HDR Reconstruction Denoising Diffusion Implicit Models

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Observation c6f2bf4b-e514-49af-a14d-c6f4862810b8 · outbound

This paper cites Alignment-free HDR Deghosting with Semantics Consistent Transformer.

GMODiff: One-Step Gain Map Refinement with Diffusion Priors for HDR Reconstruction Alignment-free HDR Deghosting with Semantics Consistent Transformer

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Observation 458cb7d2-9a1a-4361-8a8b-fe53a02c7f77 · outbound

This paper cites Lediff: Latent exposure diffusion for hdr generation.

GMODiff: One-Step Gain Map Refinement with Diffusion Priors for HDR Reconstruction Lediff: Latent exposure diffusion for hdr generation

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Observation 46b92dae-568f-479c-b556-78f6b17072e8 · outbound

This paper cites Deep learning for hdr imaging: State-of-the-art and future trends.IEEE transactions on pat- tern analysis and machine intelligence, 44(12):8874–8895,.

GMODiff: One-Step Gain Map Refinement with Diffusion Priors for HDR Reconstruction Deep learning for hdr imaging: State-of-the-art and future trends.IEEE transactions on pat- tern analysis and machine intelligence, 44(12):8874–8895,

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Observation cfe00871-a88a-40fe-8855-ca6aa70d03dc · outbound

This paper cites Prolificdreamer: High-fidelity and diverse text-to-3d generation with variational score distilla- tion.Advances in neural information processing systems, 36: 8406–8441, 2023.

GMODiff: One-Step Gain Map Refinement with Diffusion Priors for HDR Reconstruction Prolificdreamer: High-fidelity and diverse text-to-3d generation with variational score distilla- tion.Advances in neural information processing systems, 36: 8406–8441, 2023

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Observation 2460c79e-fec7-4c89-910c-cc6ca7ebe0a2 · outbound

This paper cites Deep high dynamic range imaging with large foreground motions.

GMODiff: One-Step Gain Map Refinement with Diffusion Priors for HDR Reconstruction Deep high dynamic range imaging with large foreground motions

Reference 38

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Observation 8aa3d911-e774-4da4-931e-6836f48e1770 · outbound

This paper cites Diffusion- based event generation for high-quality image deblurring.

GMODiff: One-Step Gain Map Refinement with Diffusion Priors for HDR Reconstruction Diffusion- based event generation for high-quality image deblurring

Reference 39

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Observation 11314289-1933-4bbe-845e-8f3647f7e35b · outbound

This paper cites Ufogen: You forward once large scale text-to-image gener- ation via diffusion gans.

GMODiff: One-Step Gain Map Refinement with Diffusion Priors for HDR Reconstruction Ufogen: You forward once large scale text-to-image gener- ation via diffusion gans

Reference 40

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Observation 8213256b-7e1b-48ac-ad18-f1985587654f · outbound

This paper cites Attention- guided network for ghost-free high dynamic range imaging.

GMODiff: One-Step Gain Map Refinement with Diffusion Priors for HDR Reconstruction Attention- guided network for ghost-free high dynamic range imaging

Reference 41

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Observation 598b83c0-e129-4a5f-8fb7-a044d3ae14b1 · outbound

This paper cites Deep hdr imaging via a non- local network.IEEE Transactions on Image Processing, 29: 4308–4322, 2020.

GMODiff: One-Step Gain Map Refinement with Diffusion Priors for HDR Reconstruction Deep hdr imaging via a non- local network.IEEE Transactions on Image Processing, 29: 4308–4322, 2020

Reference 42

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Observation 2abcda6b-ac82-4579-9ac0-90072487fbf4 · outbound

This paper cites A unified hdr imaging method with pixel and patch level.

GMODiff: One-Step Gain Map Refinement with Diffusion Priors for HDR Reconstruction A unified hdr imaging method with pixel and patch level

Reference 43

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Observation 79c9a228-f285-4201-9363-2017029dbd7d · outbound

This paper cites Towards high- quality hdr deghosting with conditional diffusion models.

GMODiff: One-Step Gain Map Refinement with Diffusion Priors for HDR Reconstruction Towards high- quality hdr deghosting with conditional diffusion models

Reference 44

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Observation bc5213aa-024f-4990-ba64-8ccc16b3668c · outbound

This paper cites Toward high- quality hdr deghosting with conditional diffusion models.

GMODiff: One-Step Gain Map Refinement with Diffusion Priors for HDR Reconstruction Toward high- quality hdr deghosting with conditional diffusion models

Reference 45

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Observation ddef6edb-7393-419d-af9d-57db623ccee5 · outbound

This paper cites Arbitrary-steps image super-resolution via diffusion inver- sion.

GMODiff: One-Step Gain Map Refinement with Diffusion Priors for HDR Reconstruction Arbitrary-steps image super-resolution via diffusion inver- sion

Reference 46

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Pith citing papers

Observation 947063ce-e9e8-450e-8f81-f0e81e5385f7 · inbound

Dual-Output Multi-Exposure HDR Reconstruction via SDR Fusion and Gain Map Inverse Tone Mapping cites this paper.

Dual-Output Multi-Exposure HDR Reconstruction via SDR Fusion and Gain Map Inverse Tone Mapping GMODiff: One-Step Gain Map Refinement with Diffusion Priors for HDR Reconstruction

Reference 12

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local_arxiv, observed 2026-08-07T01:10:35.574367Z

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