Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-07-09T17:59:03.869202Z
Paper Citation Record · LEDGER
Does AI Understand Imaging? A Systematic Benchmark of Agentic AI for Computational Imaging Tasks
As of 20 August 2026, this Paper Citation Record lists 60 of 60 outbound references and 0 inbound Pith citation observations for arXiv:2607.07189.
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 2026-08-20T06:33:59.587034+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
Reference resolution
60 of 60 outbound references displayed
- verified exact8
- verified fuzzy51
- unresolved0
- parse uncertain0
- malformed identifier0
- metadata mismatch1
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation 6700ba7a-b6e1-4110-852d-c330c742fb82 · outbound
This paper cites Imagenet large scale visual recognition challenge,.
Does AI Understand Imaging? A Systematic Benchmark of Agentic AI for Computational Imaging Tasks Imagenet large scale visual recognition challenge,
Reference 1
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T18:06:26.177476Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 346e2ac9-a5d6-47e2-8957-945dd56c730d · outbound
This paper cites The PASCAL visual object classes (VOC) challenge,.
Does AI Understand Imaging? A Systematic Benchmark of Agentic AI for Computational Imaging Tasks The PASCAL visual object classes (VOC) challenge,
Reference 2
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T18:06:26.205747Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 7b59c741-3a93-4efc-a0b6-14dd5fd7c1f8 · outbound
This paper cites Microsoft COCO: Common objects in context,.
Does AI Understand Imaging? A Systematic Benchmark of Agentic AI for Computational Imaging Tasks Microsoft COCO: Common objects in context,
Reference 3
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T18:06:26.201951Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 1b34f9fb-aa52-474d-8571-528c9ab939b1 · outbound
This paper cites GLUE: A multi-task benchmark and analysis platform for nat- ural language understanding,.
Does AI Understand Imaging? A Systematic Benchmark of Agentic AI for Computational Imaging Tasks GLUE: A multi-task benchmark and analysis platform for nat- ural language understanding,
Reference 4
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T18:06:26.203759Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 990b8915-c1f9-4fd8-8344-00013d35b606 · outbound
This paper cites Beyond the imitation game: Quantifying and extrapolating the capabilities of language models,.
Does AI Understand Imaging? A Systematic Benchmark of Agentic AI for Computational Imaging Tasks Beyond the imitation game: Quantifying and extrapolating the capabilities of language models,
Reference 5
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T18:06:26.207528Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 2b6b1b16-55da-436d-8834-fe9e717a4832 · outbound
This paper cites MMBench: Is your multi-modal model an all-around player?.
Does AI Understand Imaging? A Systematic Benchmark of Agentic AI for Computational Imaging Tasks MMBench: Is your multi-modal model an all-around player?
Reference 6
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T18:06:26.209227Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation a2550543-ad5b-4426-9fec-7313835a6816 · outbound
This paper cites MMMU: A massive multi-discipline multimodal understanding and reasoning benchmark for expert AGI,.
Does AI Understand Imaging? A Systematic Benchmark of Agentic AI for Computational Imaging Tasks MMMU: A massive multi-discipline multimodal understanding and reasoning benchmark for expert AGI,
Reference 7
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T18:06:26.212757Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 0cdec15f-c8a1-4976-bf08-d8d6e905b781 · outbound
This paper cites AgentBench: Evaluating LLMs as Agents.
Does AI Understand Imaging? A Systematic Benchmark of Agentic AI for Computational Imaging Tasks AgentBench: Evaluating LLMs as Agents
Reference 8
- Resolution
- verified exact
- local_arxiv, observed 2026-07-09T18:06:25.897264Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 722ab753-7a1a-41da-9add-8f3e65e3dc90 · outbound
This paper cites GAIA: A benchmark for general AI assistants,.
Does AI Understand Imaging? A Systematic Benchmark of Agentic AI for Computational Imaging Tasks GAIA: A benchmark for general AI assistants,
Reference 9
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T18:06:26.259711Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation e04f13be-3fad-462a-862b-e35eaebd4cb3 · outbound
This paper cites PhysBench: Benchmarking and Enhancing Vision-Language Models for Physical World Understanding.
Does AI Understand Imaging? A Systematic Benchmark of Agentic AI for Computational Imaging Tasks PhysBench: Benchmarking and Enhancing Vision-Language Models for Physical World Understanding
Reference 10
- Resolution
- verified exact
- local_arxiv, observed 2026-07-09T18:06:25.899637Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation be3ddc88-3b48-46a3-bea9-d82bd82f2b55 · outbound
This paper cites MMMG: a Comprehensive and Reliable Evaluation Suite for Multitask Multimodal Generation.
Does AI Understand Imaging? A Systematic Benchmark of Agentic AI for Computational Imaging Tasks MMMG: a Comprehensive and Reliable Evaluation Suite for Multitask Multimodal Generation
Reference 11
- Resolution
- verified exact
- local_arxiv, observed 2026-07-09T18:06:25.894710Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 26c37476-7342-4f84-a049-ccec25d969dd · outbound
This paper cites Com- putational imaging and artificial intelligence: The next revolution of mobile vision,.
Does AI Understand Imaging? A Systematic Benchmark of Agentic AI for Computational Imaging Tasks Com- putational imaging and artificial intelligence: The next revolution of mobile vision,
Reference 12
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T18:06:26.255267Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 2ecca5d1-a72c-4fa8-8dd1-bcf3f4d032b9 · outbound
This paper cites do: A differentiable engine for deep lens design of computational imaging systems,.
Does AI Understand Imaging? A Systematic Benchmark of Agentic AI for Computational Imaging Tasks do: A differentiable engine for deep lens design of computational imaging systems,
Reference 13
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T18:06:26.243260Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation bc0f73f7-8ef7-41b1-8d45-9aadcf80e3e6 · outbound
This paper cites Curriculum learning for ab initio deep learned refractive optics,.
Does AI Understand Imaging? A Systematic Benchmark of Agentic AI for Computational Imaging Tasks Curriculum learning for ab initio deep learned refractive optics,
Reference 14
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T18:06:26.257853Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation c9362090-6158-48c9-b156-d9208293ee82 · outbound
This paper cites Bayesian-based iterative method of image restoration.
Does AI Understand Imaging? A Systematic Benchmark of Agentic AI for Computational Imaging Tasks Bayesian-based iterative method of image restoration
Reference 15
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T18:06:26.218233Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 1e9d8944-2eb6-42d1-80c7-8686b42adc8f · outbound
This paper cites An iterative technique for the rectification of observed distributions,.
Does AI Understand Imaging? A Systematic Benchmark of Agentic AI for Computational Imaging Tasks An iterative technique for the rectification of observed distributions,
Reference 16
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T18:06:26.220058Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 888a7566-8dbb-422a-8d6f-3dbd2933e023 · outbound
This paper cites Nonlinear total variation based noise removal algorithms.
Does AI Understand Imaging? A Systematic Benchmark of Agentic AI for Computational Imaging Tasks Nonlinear total variation based noise removal algorithms
Reference 17
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T18:06:26.224275Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation b6d53c99-ec5a-4862-a1a2-8ec4f50eca6e · outbound
This paper cites Beyond a gaussian denoiser: Residual learning of deep cnn for image denoising.
Does AI Understand Imaging? A Systematic Benchmark of Agentic AI for Computational Imaging Tasks Beyond a gaussian denoiser: Residual learning of deep cnn for image denoising
Reference 18
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T18:06:26.241590Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 66c2977f-e21d-40b1-b35c-06a80ee1fc53 · outbound
This paper cites Learning a single convolutional super-resolution network for multiple degradations,.
Does AI Understand Imaging? A Systematic Benchmark of Agentic AI for Computational Imaging Tasks Learning a single convolutional super-resolution network for multiple degradations,
Reference 19
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T18:06:26.245115Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 90633634-1e19-4b51-8671-5952828f31f2 · outbound
This paper cites VQA: Visual question answering,.
Does AI Understand Imaging? A Systematic Benchmark of Agentic AI for Computational Imaging Tasks VQA: Visual question answering,
Reference 20
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T18:06:26.252911Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 71037326-94be-4186-9c64-1aa454e70d2f · outbound
This paper cites nocaps: Novel object captioning at scale,.
Does AI Understand Imaging? A Systematic Benchmark of Agentic AI for Computational Imaging Tasks nocaps: Novel object captioning at scale,
Reference 21
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T18:06:26.262164Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation c8b9309d-aecd-432d-bc0a-b767244aec02 · outbound
This paper cites MedMNIST v2: A large-scale lightweight benchmark for 2d and 3d biomedical image classification,.
Does AI Understand Imaging? A Systematic Benchmark of Agentic AI for Computational Imaging Tasks MedMNIST v2: A large-scale lightweight benchmark for 2d and 3d biomedical image classification,
Reference 22
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T18:06:26.231046Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 376e7ace-ddf9-47a6-924f-582e5ba36cbb · outbound
This paper cites CheXpert: A large chest radiograph dataset with uncertainty labels and expert comparison,.
Does AI Understand Imaging? A Systematic Benchmark of Agentic AI for Computational Imaging Tasks CheXpert: A large chest radiograph dataset with uncertainty labels and expert comparison,
Reference 23
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T18:06:26.232879Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 09255f34-29c2-4bc1-973f-d2718ad31dce · outbound
This paper cites MIMIC- CXR, a de-identified publicly available database of chest radio- graphs with free-text reports,.
Does AI Understand Imaging? A Systematic Benchmark of Agentic AI for Computational Imaging Tasks MIMIC- CXR, a de-identified publicly available database of chest radio- graphs with free-text reports,
Reference 24
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T18:06:26.227210Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation eb323f3f-a96b-4445-88c2-af9eb79847df · outbound
This paper cites VinDr-CXR: 14 An open dataset of chest x-rays with radiologist’s annotations,.
Does AI Understand Imaging? A Systematic Benchmark of Agentic AI for Computational Imaging Tasks VinDr-CXR: 14 An open dataset of chest x-rays with radiologist’s annotations,
Reference 25
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T18:06:26.259576Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation a9aa6d9f-b601-4ab2-b263-29109c0d9b1f · outbound
This paper cites The mul- timodal brain tumor image segmentation benchmark (BRATS),.
Does AI Understand Imaging? A Systematic Benchmark of Agentic AI for Computational Imaging Tasks The mul- timodal brain tumor image segmentation benchmark (BRATS),
Reference 26
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T18:06:26.229190Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 08e600f2-69a1-49f2-9717-4f0645cf1c0f · outbound
This paper cites A dataset of clinically generated visual questions and answers about radiology images,.
Does AI Understand Imaging? A Systematic Benchmark of Agentic AI for Computational Imaging Tasks A dataset of clinically generated visual questions and answers about radiology images,
Reference 27
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T18:06:26.236406Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation eb4d4d6f-dc20-4968-9174-aa03e4f11af4 · outbound
This paper cites SLAKE: A semantically-labeled knowledge-enhanced dataset for medical visual question answering,.
Does AI Understand Imaging? A Systematic Benchmark of Agentic AI for Computational Imaging Tasks SLAKE: A semantically-labeled knowledge-enhanced dataset for medical visual question answering,
Reference 28
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T18:06:26.223130Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 1bead4a3-2495-45f3-957b-ca6c96fd399c · outbound
This paper cites PathVQA: 30000+ Questions for Medical Visual Question Answering.
Does AI Understand Imaging? A Systematic Benchmark of Agentic AI for Computational Imaging Tasks PathVQA: 30000+ Questions for Medical Visual Question Answering
Reference 29
- Resolution
- verified exact
- local_arxiv, observed 2026-07-09T18:06:25.889163Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation b90e5930-5559-46af-b825-87690c36d577 · outbound
This paper cites PMC-VQA: Visual Instruction Tuning for Medical Visual Question Answering.
Does AI Understand Imaging? A Systematic Benchmark of Agentic AI for Computational Imaging Tasks PMC-VQA: Visual Instruction Tuning for Medical Visual Question Answering
Reference 30
- Resolution
- verified exact
- local_arxiv, observed 2026-07-09T18:06:25.886670Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation e9c624ac-a7b0-4683-8933-850d6b0adf39 · outbound
This paper cites GMAI-MMBench: A comprehensive multimodal evaluation benchmark towards general medical ai,.
Does AI Understand Imaging? A Systematic Benchmark of Agentic AI for Computational Imaging Tasks GMAI-MMBench: A comprehensive multimodal evaluation benchmark towards general medical ai,
Reference 31
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T18:06:26.221352Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 4952185e-10e8-481f-9ace-8e2dea54b224 · outbound
This paper cites MMMG: A massive, multidisciplinary, multi-tier generation benchmark for text-to-image reasoning,.
Does AI Understand Imaging? A Systematic Benchmark of Agentic AI for Computational Imaging Tasks MMMG: A massive, multidisciplinary, multi-tier generation benchmark for text-to-image reasoning,
Reference 32
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T18:06:26.219623Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 4de0ace4-cacd-41ae-bef1-5e8977aaa3b1 · outbound
This paper cites Mmmg: A massive, multidisciplinary, multi-tier gener- ation benchmark for text-to-image reasoning.
Does AI Understand Imaging? A Systematic Benchmark of Agentic AI for Computational Imaging Tasks Mmmg: A massive, multidisciplinary, multi-tier gener- ation benchmark for text-to-image reasoning
Reference 33
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-07-09T18:06:25.892220Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 102f41d4-62ff-494a-ba01-105fb55e4f81 · outbound
This paper cites Hybrid optical-electronic convolutional neural networks with optimized diffractive optics for image classification,.
Does AI Understand Imaging? A Systematic Benchmark of Agentic AI for Computational Imaging Tasks Hybrid optical-electronic convolutional neural networks with optimized diffractive optics for image classification,
Reference 34
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T18:06:26.238112Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 5f590d8f-be43-4f95-a7b7-499ebef2b9b4 · outbound
This paper cites Vision-language model guided image restoration.
Does AI Understand Imaging? A Systematic Benchmark of Agentic AI for Computational Imaging Tasks Vision-language model guided image restoration
Reference 35
- Resolution
- verified exact
- arxiv_id, observed 2026-07-09T18:06:25.880312Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 8116c6f1-dc2a-4068-baa7-6ea0676c321e · outbound
This paper cites Optiagent: A physics-driven agentic framework for automated optical design,.
Does AI Understand Imaging? A Systematic Benchmark of Agentic AI for Computational Imaging Tasks Optiagent: A physics-driven agentic framework for automated optical design,
Reference 36
- Resolution
- verified exact
- arxiv_id, observed 2026-07-09T18:06:25.883101Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 416452c3-42f4-4836-8095-3f74c52e47ef · outbound
This paper cites Towards real- time photorealistic 3d holography with deep neural networks,.
Does AI Understand Imaging? A Systematic Benchmark of Agentic AI for Computational Imaging Tasks Towards real- time photorealistic 3d holography with deep neural networks,
Reference 37
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T18:06:26.239902Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation b1ab8075-ac59-4fac-affe-11ea2776219c · outbound
This paper cites Uni- fied reconstruction of static and dynamic scenes from events,.
Does AI Understand Imaging? A Systematic Benchmark of Agentic AI for Computational Imaging Tasks Uni- fied reconstruction of static and dynamic scenes from events,
Reference 38
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T18:06:26.268124Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation cbe624a4-8716-4f93-9f7d-e62a95d47c68 · outbound
This paper cites Depth restoration in under-display time-of-flight imaging,.
Does AI Understand Imaging? A Systematic Benchmark of Agentic AI for Computational Imaging Tasks Depth restoration in under-display time-of-flight imaging,
Reference 39
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T18:06:26.204085Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation a1086176-4f4a-4f0f-b0e3-22bba2de65fe · outbound
This paper cites Learned reconstructions for practical mask-based lens- less imaging,.
Does AI Understand Imaging? A Systematic Benchmark of Agentic AI for Computational Imaging Tasks Learned reconstructions for practical mask-based lens- less imaging,
Reference 40
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T18:06:26.210107Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 57a91108-cfe6-4641-983a-c98695fa6dde · outbound
This paper cites When color constancy goes wrong: Correcting improperly white-balanced im- ages,.
Does AI Understand Imaging? A Systematic Benchmark of Agentic AI for Computational Imaging Tasks When color constancy goes wrong: Correcting improperly white-balanced im- ages,
Reference 41
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T18:06:26.254583Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation b27f48f5-e059-4cc7-aee0-e11fc8d7dc24 · outbound
This paper cites Beyond joint demosaick- ing and denoising: An image processing pipeline for a pixel-bin image sensor,.
Does AI Understand Imaging? A Systematic Benchmark of Agentic AI for Computational Imaging Tasks Beyond joint demosaick- ing and denoising: An image processing pipeline for a pixel-bin image sensor,
Reference 42
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T18:06:26.211835Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation cd06ae6a-276b-4f1a-9e19-d8be76660af8 · outbound
This paper cites Burst photography for high dynamic range and low-light imaging on mobile cameras,.
Does AI Understand Imaging? A Systematic Benchmark of Agentic AI for Computational Imaging Tasks Burst photography for high dynamic range and low-light imaging on mobile cameras,
Reference 43
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T18:06:26.228487Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation e38bf1cf-2446-41cb-9c5f-7670bd575c50 · outbound
This paper cites Fpa-cs: Focal plane array-based compressive imaging in short-wave infrared,.
Does AI Understand Imaging? A Systematic Benchmark of Agentic AI for Computational Imaging Tasks Fpa-cs: Focal plane array-based compressive imaging in short-wave infrared,
Reference 44
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T18:06:26.234643Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation d8164e0c-c29c-43bf-937e-fd5d7366c226 · outbound
This paper cites Resolution-robust large mask inpainting with fourier convolutions,.
Does AI Understand Imaging? A Systematic Benchmark of Agentic AI for Computational Imaging Tasks Resolution-robust large mask inpainting with fourier convolutions,
Reference 45
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T18:06:26.264323Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 16ab13ac-4c1b-4773-984b-6604fddcf02c · outbound
This paper cites Gemini 3.1 flash-image model card,.
Does AI Understand Imaging? A Systematic Benchmark of Agentic AI for Computational Imaging Tasks Gemini 3.1 flash-image model card,
Reference 46
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T18:06:26.252912Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation e1a8dcf3-fff3-4e9c-8114-eda0b0bf59bb · outbound
This paper cites [Online].
Does AI Understand Imaging? A Systematic Benchmark of Agentic AI for Computational Imaging Tasks [Online]
Reference 47
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T18:06:26.256194Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation d88b1fc7-20a4-463d-8273-b2e6c77ab049 · outbound
This paper cites Qwen-Image Technical Report.
Does AI Understand Imaging? A Systematic Benchmark of Agentic AI for Computational Imaging Tasks Qwen-Image Technical Report
Reference 48
- Resolution
- verified exact
- local_arxiv, observed 2026-07-09T18:06:25.888870Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation dd7e56c5-44c8-44fd-817c-b132190e2764 · outbound
This paper cites A high-quality de- noising dataset for smartphone cameras,.
Does AI Understand Imaging? A Systematic Benchmark of Agentic AI for Computational Imaging Tasks A high-quality de- noising dataset for smartphone cameras,
Reference 49
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T18:06:26.246030Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 27a083a6-956c-44b3-ab83-0c1bced2390c · outbound
This paper cites Image demosaicing: A system- atic survey,.
Does AI Understand Imaging? A Systematic Benchmark of Agentic AI for Computational Imaging Tasks Image demosaicing: A system- atic survey,
Reference 50
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T18:06:26.257324Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 6cfb8cbf-707f-4413-8488-43a57cccd54e · outbound
This paper cites Deep multi-scale convolutional neural network for dynamic scene deblurring,.
Does AI Understand Imaging? A Systematic Benchmark of Agentic AI for Computational Imaging Tasks Deep multi-scale convolutional neural network for dynamic scene deblurring,
Reference 51
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T18:06:26.266208Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation b8c7aed1-4d33-4ad0-926d-c8793a3fb10d · outbound
This paper cites The stanford light field archive (2016),.
Does AI Understand Imaging? A Systematic Benchmark of Agentic AI for Computational Imaging Tasks The stanford light field archive (2016),
Reference 52
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T18:06:26.249641Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation c5955224-600a-41ca-826c-78c945fc0595 · outbound
This paper cites Ntire 2017 challenge on single image super-resolution: Dataset and study,.
Does AI Understand Imaging? A Systematic Benchmark of Agentic AI for Computational Imaging Tasks Ntire 2017 challenge on single image super-resolution: Dataset and study,
Reference 53
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T18:06:26.242261Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 333df7e2-3e21-40aa-81ca-cbe477f7b316 · outbound
This paper cites Learning photo- graphic global tonal adjustment with a database of input / output image pairs,.
Does AI Understand Imaging? A Systematic Benchmark of Agentic AI for Computational Imaging Tasks Learning photo- graphic global tonal adjustment with a database of input / output image pairs,
Reference 54
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T18:06:26.237141Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation d253141f-a857-412b-b2e1-1cd89da23762 · outbound
This paper cites Phasecam3d—learning phase masks for pas- sive single view depth estimation,.
Does AI Understand Imaging? A Systematic Benchmark of Agentic AI for Computational Imaging Tasks Phasecam3d—learning phase masks for pas- sive single view depth estimation,
Reference 55
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T18:06:26.235351Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 7f42f18a-4f66-4c7b-a756-9e8410b66755 · outbound
This paper cites A physics-based noise formation model for extreme low-light raw denoising,.
Does AI Understand Imaging? A Systematic Benchmark of Agentic AI for Computational Imaging Tasks A physics-based noise formation model for extreme low-light raw denoising,
Reference 56
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T18:06:26.238874Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 9e8e6e20-5552-4905-862e-e4cc08198035 · outbound
This paper cites Deep white-balance editing,.
Does AI Understand Imaging? A Systematic Benchmark of Agentic AI for Computational Imaging Tasks Deep white-balance editing,
Reference 57
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T18:06:26.231874Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 0074a96d-4fcb-4e3a-9894-85de9270e071 · outbound
This paper cites Ffdnet: Toward a fast and flexible solution for cnn-based image denoising,.
Does AI Understand Imaging? A Systematic Benchmark of Agentic AI for Computational Imaging Tasks Ffdnet: Toward a fast and flexible solution for cnn-based image denoising,
Reference 58
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T18:06:26.230291Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 7cac7737-0614-4338-9303-0e4a534a4436 · outbound
This paper cites Banet: A blur-aware attention network for dynamic scene deblurring,.
Does AI Understand Imaging? A Systematic Benchmark of Agentic AI for Computational Imaging Tasks Banet: A blur-aware attention network for dynamic scene deblurring,
Reference 59
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T18:06:26.233559Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 3bd3cf83-aba0-499c-aeb7-c0fade8fc54e · outbound
This paper cites Esrgan: Enhanced super-resolution generative adversarial networks,.
Does AI Understand Imaging? A Systematic Benchmark of Agentic AI for Computational Imaging Tasks Esrgan: Enhanced super-resolution generative adversarial networks,
Reference 60
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T18:06:26.240585Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Pith citing papers
No inbound Pith citation observations are available.