MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices | Paper Citation Record

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This paper cites Bias loss for mobile neural networks.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices Bias loss for mobile neural networks

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This paper cites Uw- mamba: Underwater image enhancement with state space model.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices Uw- mamba: Underwater image enhancement with state space model

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This paper cites Beyond self-attention: Deformable large kernel attention for medi- cal image segmentation.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices Beyond self-attention: Deformable large kernel attention for medi- cal image segmentation

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This paper cites Retinexmamba: Retinex-based Mamba for Low-light Image Enhancement.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices Retinexmamba: Retinex-based Mamba for Low-light Image Enhancement

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This paper cites A general and adaptive robust loss func- tion.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices A general and adaptive robust loss func- tion

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

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices Retinexformer: One-stage retinex-based transformer for low-light image enhance- ment

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This paper cites RefConv: Re-parameterized Refocusing Convolution for Powerful ConvNets.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices RefConv: Re-parameterized Refocusing Convolution for Powerful ConvNets

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This paper cites Vanillanet: the power of minimalism in deep learning.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices Vanillanet: the power of minimalism in deep learning

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This paper cites Run, don’t walk: chasing higher flops for faster neural networks.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices Run, don’t walk: chasing higher flops for faster neural networks

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This paper cites Simple baselines for image restoration.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices Simple baselines for image restoration

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This paper cites Mofa: A model simplification roadmap for image restoration on mobile devices.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices Mofa: A model simplification roadmap for image restoration on mobile devices

Reference 11

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This paper cites Gcam: lightweight image inpainting via group convolution and attention mechanism.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices Gcam: lightweight image inpainting via group convolution and attention mechanism

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This paper cites MambaUIE&SR: Unraveling the Ocean's Secrets with Only 2.8 GFLOPs.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices MambaUIE&SR: Unraveling the Ocean's Secrets with Only 2.8 GFLOPs

Reference 13

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This paper cites Dea-net: Single image dehazing based on detail-enhanced convolution and content-guided attention.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices Dea-net: Single image dehazing based on detail-enhanced convolution and content-guided attention

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MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices Reciprocal attention mixing transformer for lightweight image restoration

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This paper cites Efficient deep models for real-time 4k image super-resolution.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices Efficient deep models for real-time 4k image super-resolution

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This paper cites Focal network for image restoration.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices Focal network for image restoration

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This paper cites Image restoration via frequency selection.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices Image restoration via frequency selection

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This paper cites Revitalizing convolutional network for image restoration.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices Revitalizing convolutional network for image restoration

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This paper cites You only need 90k parameters to adapt light: a light weight trans- former for image enhancement and exposure correction.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices You only need 90k parameters to adapt light: a light weight trans- former for image enhancement and exposure correction

Reference 20

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This paper cites Awnet: Attentive wavelet network for image isp.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices Awnet: Attentive wavelet network for image isp

Reference 21

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This paper cites Acnet: Strengthening the kernel skeletons for power- ful cnn via asymmetric convolution blocks.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices Acnet: Strengthening the kernel skeletons for power- ful cnn via asymmetric convolution blocks

Reference 22

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This paper cites Diverse branch block: Building a con- volution as an inception-like unit.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices Diverse branch block: Building a con- volution as an inception-like unit

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Observation 14a1a361-94f6-496a-9f8f-43f0ec5e51c2 · outbound

This paper cites Repvgg: Making vgg- style convnets great again.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices Repvgg: Making vgg- style convnets great again

Reference 24

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Observation 901aa690-feac-48b7-bb09-d7429efc43ef · outbound

This paper cites Un- derwater depth estimation and image restoration based on single images.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices Un- derwater depth estimation and image restoration based on single images

Reference 25

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This paper cites Uncertainty inspired underwater image en- hancement.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices Uncertainty inspired underwater image en- hancement

Reference 26

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This paper cites Learning a simple low-light im- age enhancer from paired low-light instances.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices Learning a simple low-light im- age enhancer from paired low-light instances

Reference 27

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This paper cites Syenet: A simple yet effective net- work for multiple low-level vision tasks with real-time per- formance on mobile device.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices Syenet: A simple yet effective net- work for multiple low-level vision tasks with real-time per- formance on mobile device

Reference 28

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This paper cites Zero- reference deep curve estimation for low-light image en- hancement.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices Zero- reference deep curve estimation for low-light image en- hancement

Reference 29

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MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices Ghostnet: More features from cheap operations

Reference 30

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This paper cites Masked autoencoders are scal- able vision learners.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices Masked autoencoders are scal- able vision learners

Reference 31

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MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices Enhancing raw-to-srgb with decoupled style structure in fourier domain

Reference 32

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MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices Searching for mo- bilenetv3

Reference 33

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MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices Squeeze-and-excitation networks

Reference 34

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This paper cites Aim 2020 challenge on learned image signal processing pipeline.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices Aim 2020 challenge on learned image signal processing pipeline

Reference 35

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This paper cites Replac- ing mobile camera isp with a single deep learning model.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices Replac- ing mobile camera isp with a single deep learning model

Reference 36

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This paper cites Learned smartphone isp on mobile npus with deep learning, mobile ai 2021 chal- lenge: Report.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices Learned smartphone isp on mobile npus with deep learning, mobile ai 2021 chal- lenge: Report

Reference 37

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This paper cites Learned smartphone isp on mobile gpus with deep learning, mobile ai & aim 2022 challenge: report.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices Learned smartphone isp on mobile gpus with deep learning, mobile ai & aim 2022 challenge: report

Reference 38

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This paper cites Fast un- derwater image enhancement for improved visual percep- tion.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices Fast un- derwater image enhancement for improved visual percep- tion

Reference 39

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This paper cites Low-light image enhancement with wavelet-based diffusion models.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices Low-light image enhancement with wavelet-based diffusion models

Reference 40

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This paper cites Five a+ net- work: You only need 9k parameters for underwater image enhancement.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices Five a+ net- work: You only need 9k parameters for underwater image enhancement

Reference 41

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This paper cites Spectroformer: Multi-domain query cascaded transformer network for underwater image enhancement.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices Spectroformer: Multi-domain query cascaded transformer network for underwater image enhancement

Reference 42

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This paper cites Feature modulation transformer: Cross-refinement of global representation via high-frequency prior for image super-resolution.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices Feature modulation transformer: Cross-refinement of global representation via high-frequency prior for image super-resolution

Reference 43

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This paper cites An underwater image enhancement benchmark dataset and beyond.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices An underwater image enhancement benchmark dataset and beyond

Reference 44

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Observation 41b48644-296a-40d2-adfe-b839a1c5b94b · outbound

This paper cites Learning to enhance low-light image via zero-reference deep curve estimation.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices Learning to enhance low-light image via zero-reference deep curve estimation

Reference 45

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This paper cites Ntire 2023 challenge on efficient super- resolution: Methods and results.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices Ntire 2023 challenge on efficient super- resolution: Methods and results

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:47:12.905832Z

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This paper cites Retinex-inspired unrolling with cooperative prior architecture search for low-light image enhancement.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices Retinex-inspired unrolling with cooperative prior architecture search for low-light image enhancement

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:47:12.888552Z

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Observation 730c672f-36ba-4aac-a817-d02bbf3e6d74 · outbound

This paper cites Boths: Super lightweight network-enabled under- water image enhancement.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices Boths: Super lightweight network-enabled under- water image enhancement

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:47:12.867407Z

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Observation 2af5d7de-ee41-46d9-97db-93bf42f1945c · outbound

This paper cites NTIRE 2024 Challenge on Low Light Image Enhancement: Methods and Results.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices NTIRE 2024 Challenge on Low Light Image Enhancement: Methods and Results

Reference 49

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This paper cites NAM: Normalization-based Attention Module.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices NAM: Normalization-based Attention Module

Reference 50

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This paper cites Toward fast, flexible, and robust low-light image enhancement.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices Toward fast, flexible, and robust low-light image enhancement

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:47:12.851369Z

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Observation 4964214c-f3b7-449f-9732-be9e06a5eded · outbound

This paper cites Rewrite the stars.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices Rewrite the stars

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:47:12.834577Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation c0d7d973-58bf-4129-b0c3-66d197cbe491 · outbound

This paper cites A wavelet-based dual-stream network for underwater image enhancement.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices A wavelet-based dual-stream network for underwater image enhancement

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:47:12.818436Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 49c11520-daab-40a4-8916-bbe6dee0bc3d · outbound

This paper cites Shallow-uwnet: Compressed model for underwater image enhancement (student abstract).

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices Shallow-uwnet: Compressed model for underwater image enhancement (student abstract)

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:47:12.801082Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation aaefe1b2-76ba-432a-8a00-bfa61a389030 · outbound

This paper cites Efficient multi-scale attention module with cross-spatial learning.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices Efficient multi-scale attention module with cross-spatial learning

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:47:12.785168Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 1d5a147b-b015-49da-8c5d-19b557240fea · outbound

This paper cites U-shape trans- former for underwater image enhancement.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices U-shape trans- former for underwater image enhancement

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:47:12.769459Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 7e9a4182-50ed-49c0-ab4c-debfb5c1b90c · outbound

This paper cites Rawformer: Unpaired Raw-to-Raw Translation for Learnable Camera ISPs.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices Rawformer: Unpaired Raw-to-Raw Translation for Learnable Camera ISPs

Reference 57

Resolution: verified exact; local_arxiv, observed 2026-08-06T20:47:11.919695Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 47d03f55-278d-43b0-96bb-289261a0d249 · outbound

This paper cites Semi- supervised feature distillation and unsupervised domain ad- versarial distillation for underwater image enhancement.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices Semi- supervised feature distillation and unsupervised domain ad- versarial distillation for underwater image enhancement

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:47:12.754332Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 26f94d76-bbee-415c-9af7-30b47a9bc5a0 · outbound

This paper cites Double domain guided real- time low-light image enhancement for ultra-high-definition transportation surveillance.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices Double domain guided real- time low-light image enhancement for ultra-high-definition transportation surveillance

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:47:12.739379Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation d4e0893d-8c49-4cbe-8d0d-32ee3b79954d · outbound

This paper cites Quantized proximal averaging networks for com- pressed image recovery.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices Quantized proximal averaging networks for com- pressed image recovery

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:47:12.723497Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 4a62d7b6-d4f6-4ce6-8e27-40b6fcf6f1ea · outbound

This paper cites The ninth ntire 2024 efficient super- resolution challenge report.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices The ninth ntire 2024 efficient super- resolution challenge report

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:47:12.708312Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 4932f085-7259-4051-a1a6-ddbf3afb891c · outbound

This paper cites Wavelength- based attributed deep neural network for underwater image restoration.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices Wavelength- based attributed deep neural network for underwater image restoration

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:47:12.693032Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 04c8e027-2eb8-4511-b2a2-e915b47f2663 · outbound

This paper cites Efficient attention: Attention with lin- ear complexities.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices Efficient attention: Attention with lin- ear complexities

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:47:12.677409Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 2699ab60-0d03-4b1f-ab11-f7c9b4e53114 · outbound

This paper cites Memory-oriented structural pruning for efficient im- age restoration.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices Memory-oriented structural pruning for efficient im- age restoration

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:47:12.660260Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 7990f307-1a5f-4321-8e59-01cab9b8407a · outbound

This paper cites Ghostnetv2: Enhance cheap operation with long-range attention.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices Ghostnetv2: Enhance cheap operation with long-range attention

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:47:12.641148Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation a5092599-deca-43ba-ac2f-37fcd60baeca · outbound

This paper cites Un- derwater image enhancement by transformer-based diffu- sion model with non-uniform sampling for skip strategy.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices Un- derwater image enhancement by transformer-based diffu- sion model with non-uniform sampling for skip strategy

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:47:12.622922Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 05671936-cedd-47fd-b082-5dea3f16d8af · outbound

This paper cites Mobileone: An improved one millisecond mobile backbone.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices Mobileone: An improved one millisecond mobile backbone

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:47:12.602920Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation f4a7b53a-fb6e-40bc-9e8e-972bdf414c72 · outbound

This paper cites Swift parameter-free attention network for efficient super- resolution.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices Swift parameter-free attention network for efficient super- resolution

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:47:12.587579Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation c1b9dae6-6a95-44e6-accd-7786961307c0 · outbound

This paper cites Repvit: Revisiting mobile cnn from vit perspective.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices Repvit: Revisiting mobile cnn from vit perspective

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:47:12.571066Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation e161b269-9d31-4a61-b282-f8252d2a845a · outbound

This paper cites Cor- relation matching transformation transformers for uhd im- age restoration.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices Cor- relation matching transformation transformers for uhd im- age restoration

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:47:12.551335Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 3cbc2d1c-01ac-4677-b0e3-13344bec8cbc · outbound

This paper cites Eca-net: Efficient channel attention for deep convolutional neural networks.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices Eca-net: Efficient channel attention for deep convolutional neural networks

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:47:12.530620Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 800abd42-0e73-4e6b-be1e-016597028e4d · outbound

This paper cites Ultra-high-definition low-light image enhancement: A benchmark and transformer-based method.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices Ultra-high-definition low-light image enhancement: A benchmark and transformer-based method

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:47:12.513533Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation e26ed637-578b-454e-b1b4-1a44d93f1806 · outbound

This paper cites Adversar- ially regularized low-light image enhancement.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices Adversar- ially regularized low-light image enhancement

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:47:12.493985Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation a121da83-297e-4798-94d1-375fd73cfb1d · outbound

This paper cites Tied block convolution: Leaner and better cnns with shared thinner filters.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices Tied block convolution: Leaner and better cnns with shared thinner filters

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:47:12.477602Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 97d991aa-154d-409f-bfa0-d55cd059757c · outbound

This paper cites Repsr: Training efficient vgg-style super-resolution networks with structural re-parameterization and batch normalization.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices Repsr: Training efficient vgg-style super-resolution networks with structural re-parameterization and batch normalization

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:47:12.462590Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation fb12a3c7-f668-415d-9baa-029e183d00e8 · outbound

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

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices Deep retinex decomposition for low-light enhancement

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:47:12.447554Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 8fda7d9c-9410-4bba-9157-10c2fd31ab1c · outbound

This paper cites An illumination-guided dual attention vision transformer for low-light image enhancement.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices An illumination-guided dual attention vision transformer for low-light image enhancement

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:47:12.431203Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 227be130-f876-4123-9dfa-6a4a5bd42dac · outbound

This paper cites Cbam: Convolutional block attention module.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices Cbam: Convolutional block attention module

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:47:12.415463Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 38093d8a-9615-43a1-90df-270fdc7b8489 · outbound

This paper cites Uretinex-net: Retinex-based deep unfolding network for low-light image enhancement.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices Uretinex-net: Retinex-based deep unfolding network for low-light image enhancement

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:47:12.400451Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 67ff739c-51ee-4ce2-b17e-04393d820e22 · outbound

This paper cites Diffir: Efficient diffusion model for image restoration.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices Diffir: Efficient diffusion model for image restoration

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:47:12.384377Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 254499e8-6592-4076-9609-024f95da6f44 · outbound

This paper cites Boosting image restoration via priors from pre-trained models.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices Boosting image restoration via priors from pre-trained models

Reference 81

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:47:12.363918Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation e02752bd-d1aa-4d4d-ade1-195c09fbae1c · outbound

This paper cites From fidelity to perceptual quality: A semi- supervised approach for low-light image enhancement.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices From fidelity to perceptual quality: A semi- supervised approach for low-light image enhancement

Reference 82

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:47:12.345467Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation fefc34f9-54f3-4cfc-9527-8b8bb80df259 · outbound

This paper cites Accelir: Task-aware image compression for accelerating neural restoration.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices Accelir: Task-aware image compression for accelerating neural restoration

Reference 83

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:47:12.329488Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 8d98691a-77b9-48f1-a3c3-feb9d1eeef88 · outbound

This paper cites Diffraw: Leveraging diffusion model to generate dslr- comparable perceptual quality srgb from smartphone raw images.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices Diffraw: Leveraging diffusion model to generate dslr- comparable perceptual quality srgb from smartphone raw images

Reference 84

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:47:12.313073Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation c43df305-9d51-4e77-b626-01007652102b · outbound

This paper cites Diff-retinex: Rethinking low-light image enhancement with a generative diffusion model.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices Diff-retinex: Rethinking low-light image enhancement with a generative diffusion model

Reference 85

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:47:12.296442Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation fda1d60f-02ec-467a-9a8f-98caf64cdf0b · outbound

This paper cites Learning enriched features for real image restora- tion and enhancement.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices Learning enriched features for real image restora- tion and enhancement

Reference 86

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:47:12.278811Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 54a25b44-d048-41a3-8747-b3ccd91e64e4 · outbound

This paper cites Learning image-adaptive 3d lookup tables for high performance photo enhancement in real-time.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices Learning image-adaptive 3d lookup tables for high performance photo enhancement in real-time

Reference 87

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:47:12.260482Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation c271737c-a15f-4295-940d-6cde7ffa532b · outbound

This paper cites Rethinking mobile block for efficient attention-based models.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices Rethinking mobile block for efficient attention-based models

Reference 88

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:47:12.242098Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation e516f4cc-1be4-4f1e-a7e8-d54f478e6d9a · outbound

This paper cites Repnas: Searching for efficient re-parameterizing blocks.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices Repnas: Searching for efficient re-parameterizing blocks

Reference 89

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:47:12.223563Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 6b5ad6b3-8731-400a-a7d4-cf8ea33dbd89 · outbound

This paper cites Liteenhancenet: A lightweight network for real-time single underwater image enhancement.Expert Systems with Applications, 240:122546, 2024.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices Liteenhancenet: A lightweight network for real-time single underwater image enhancement.Expert Systems with Applications, 240:122546, 2024

Reference 90

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:47:12.208494Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation f108411c-a280-4562-984e-e567414ff67d · outbound

This paper cites Shufflenet: An extremely efficient convolutional neural net- work for mobile devices.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices Shufflenet: An extremely efficient convolutional neural net- work for mobile devices

Reference 91

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:47:12.192120Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 48dc6547-86ad-4832-8d6e-8ef3d0d0a194 · outbound

This paper cites Edge-oriented convolution block for real-time super resolution on mobile devices.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices Edge-oriented convolution block for real-time super resolution on mobile devices

Reference 92

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:47:12.174036Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 8444728b-ed26-4bf5-8d40-225dad4fa741 · outbound

This paper cites LLEMamba: Low-Light Enhancement via Relighting-Guided Mamba with Deep Unfolding Network.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices LLEMamba: Low-Light Enhancement via Relighting-Guided Mamba with Deep Unfolding Network

Reference 93

Resolution: unresolved; no resolver link, observed 2026-08-06T20:47:11.785971Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 93d61435-8de9-4aea-9cd8-d1a63b03a132 · outbound

This paper cites Beyond brightening low-light images.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices Beyond brightening low-light images

Reference 94

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:47:12.155712Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 3cd9a218-dd66-4b3c-ba24-55127c268761 · outbound

This paper cites Learning raw-to-srgb mappings with inaccurately aligned supervision.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices Learning raw-to-srgb mappings with inaccurately aligned supervision

Reference 95

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:47:12.137202Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation cfa5fdde-77c1-417d-bf0b-4cb5b9cd3b3b · outbound

This paper cites Wavelet-based fourier information interaction with fre- quency diffusion adjustment for underwater image restora- tion.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices Wavelet-based fourier information interaction with fre- quency diffusion adjustment for underwater image restora- tion

Reference 96

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:47:12.119738Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation a5b761d3-45c8-4185-950a-9fd25768d333 · outbound

This paper cites To- ward sufficient spatial-frequency interaction for gradient- aware underwater image enhancement.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices To- ward sufficient spatial-frequency interaction for gradient- aware underwater image enhancement

Reference 97

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:47:12.101228Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 48c14eda-ad24-4c06-afce-8617b9794f21 · outbound

This paper cites Semantic-guided zero-shot learning for low-light image/video enhancement.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices Semantic-guided zero-shot learning for low-light image/video enhancement

Reference 98

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:47:12.081244Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 96133384-9eaf-4a48-914d-dcb206cc047c · outbound

This paper cites A 7K Parameter Model for Underwater Image Enhancement based on Transmission Map Prior.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices A 7K Parameter Model for Underwater Image Enhancement based on Transmission Map Prior

Reference 99

Resolution: unresolved; no resolver link, observed 2026-08-06T20:47:11.819282Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3f4f00a9-08eb-44dd-9694-537c1b85e94c · outbound

This paper cites Ac- celerate cnn via recursive bayesian pruning.

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices Ac- celerate cnn via recursive bayesian pruning

Reference 100

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:47:12.064810Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers