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 526–550 of 983 papers

TitleStatusHype
An End-to-End OCR Framework for Robust Arabic-Handwriting Recognition using a Novel Transformers-based Model and an Innovative 270 Million-Words Multi-Font Corpus of Classical Arabic with Diacritics—0
Low-light Enhancement Method Based on Attention Map Net—0
Local Low-light Image Enhancement via Region-Aware Normalization—0
HighlightNet: Highlighting Low-Light Potential Features for Real-Time UAV TrackingCode1
Underwater Ranker: Learn Which Is Better and How to Be BetterCode1
Learning Hierarchical Dynamics with Spatial Adjacency for Image EnhancementCode0
Low-Light Hyperspectral Image EnhancementCode1
Underwater enhancement based on a self-learning strategy and attention mechanism for high-intensity regions—0
CuDi: Curve Distillation for Efficient and Controllable Exposure Adjustment—0
Unsupervised Night Image Enhancement: When Layer Decomposition Meets Light-Effects SuppressionCode2
DC-ShadowNet: Single-Image Hard and Soft Shadow Removal Using Unsupervised Domain-Classifier Guided NetworkCode2
Towards Efficient and Scale-Robust Ultra-High-Definition Image DemoireingCode2
Uncertainty Inspired Underwater Image EnhancementCode1
SepLUT: Separable Image-adaptive Lookup Tables for Real-time Image EnhancementCode1
Neural Color Operators for Sequential Image RetouchingCode1
Structural Prior Guided Generative Adversarial Transformers for Low-Light Image Enhancement—0
Enhancement by Your Aesthetic: An Intelligible Unsupervised Personalized Enhancer for Low-Light Images—0
Explaining Image Enhancement Black-Box Methods through a Path Planning Based AlgorithmCode0
6D Camera Relocalization in Visually Ambiguous Extreme Environments—0
On the limits of perceptual quality measures for enhanced underwater images—0
Going the Extra Mile in Face Image Quality Assessment: A Novel Database and Model—0
A Novel Hybrid Endoscopic Dataset for Evaluating Machine Learning-based Photometric Image Enhancement Models—0
Cycle-Interactive Generative Adversarial Network for Robust Unsupervised Low-Light Enhancement—0
Face Image Lighting Enhancement Using a 3D Model—0
A novel approach for glaucoma classification by wavelet neural networks using graph-based, statisitcal features of qualitatively improved images—0
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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