SOTAVerified

Image Enhancement

Image Enhancement is basically improving the interpretability or perception of information in images for human viewers and providing ‘better’ input for other automated image processing techniques. The principal objective of Image Enhancement is to modify attributes of an image to make it more suitable for a given task and a specific observer.

Source: A Comprehensive Review of Image Enhancement Techniques

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 851–875 of 983 papers

TitleStatusHype
MetaUE: Model-based Meta-learning for Underwater Image EnhancementCode0
Medium Transmission Map Matters for Learning to Restore Real-World Underwater ImagesCode0
EENet: Frequency-Aware and Spatially Multiscale Network for Single Image DehazingCode0
MambaUIE&SR: Unraveling the Ocean's Secrets with Only 2.8 GFLOPsCode0
EdgeFool: An Adversarial Image Enhancement FilterCode0
WaterMono: Teacher-Guided Anomaly Masking and Enhancement Boosting for Robust Underwater Self-Supervised Monocular Depth EstimationCode0
Mamba-UIE: Enhancing Underwater Images with Physical Model ConstraintCode0
Privacy-Preserving Pose Estimation for Human-Robot InteractionCode0
Anatomically Constrained Neural Networks (ACNN): Application to Cardiac Image Enhancement and SegmentationCode0
Projected Distribution Loss for Image EnhancementCode0
Analyzing Noise Models and Advanced Filtering Algorithms for Image EnhancementCode0
Edge-Computing-Enabled Deep Learning Approach for Low-Light Satellite Image EnhancementCode0
MAC-Lookup: Multi-Axis Conditional Lookup Model for Underwater Image EnhancementCode0
Luminance-Guided Chrominance Image Enhancement for HEVC Intra CodingCode0
Color Image Enhancement Using the Support Fuzzification in the Framework of the Logarithmic ModelCode0
Low-light Stereo Image Enhancement and De-noising in the Low-frequency Information Enhanced Image SpaceCode0
PUGAN: Physical Model-Guided Underwater Image Enhancement Using GAN with Dual-DiscriminatorsCode0
Low-Light Image Enhancement Based on Cell Vibration Energy Model and Lightness DifferenceCode0
Color Image Enhancement Using the lrgb Coordinates in the Context of Support FuzzificationCode0
Clearing the Skies: A deep network architecture for single-image rain removalCode0
All-In-One Underwater Image Enhancement using Domain-Adversarial LearningCode0
Quality-guided Skin Tone Enhancement for Portrait PhotographyCode0
Chest X-ray Image Phase Features for Improved Diagnosis of COVID-19 Using Convolutional Neural NetworkCode0
ECAFormer: Low-light Image Enhancement using Cross AttentionCode0
Text in the Dark: Extremely Low-Light Text Image EnhancementCode0
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All datasetsMIT-Adobe 5kTIP 2018Exposure-ErrorsMIT-Adobe FiveKSICE-GradSICE-Mix

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HG-MTFEPSNR on proRGB25.69—Unverified
2PQDynamicISPPSNR on proRGB25.53—Unverified
3RSFNet-mapPSNR on proRGB25.49—Unverified
4AdaIntPSNR on proRGB25.49—Unverified
5SepLUTPSNR on proRGB25.47—Unverified
6MTFEPSNR on proRGB25.46—Unverified
73D LUTPSNR on proRGB25.21—Unverified
8RetinexformerPSNR on sRGB24.94—Unverified
94D LUTPSNR on proRGB24.61—Unverified
10DIFAR (MSCA, level 1)PSNR on proRGB24.2—Unverified
#ModelMetricClaimedVerifiedStatus
1ESDNet-LPSNR30.11—Unverified
2MBCNNPSNR30.03—Unverified
3ESDNetPSNR29.81—Unverified
4Uformer-BPSNR29.28—Unverified
5MopNetPSNR27.75—Unverified
6DMCNNPSNR26.77—Unverified
#ModelMetricClaimedVerifiedStatus
1Exposure-slotPSNR23.18—Unverified
2CSECPSNR22.73—Unverified
3LCDPNetPSNR22.17—Unverified
4IATPSNR20.34—Unverified
5MSECPSNR20.21—Unverified
#ModelMetricClaimedVerifiedStatus
1TreEnhanceDeltaE11.25—Unverified
#ModelMetricClaimedVerifiedStatus
1CIDNetAverage PSNR13.45—Unverified
#ModelMetricClaimedVerifiedStatus
1CIDNetAverage PSNR13.43—Unverified