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 26–50 of 983 papers

TitleStatusHype
Ultra-High-Definition Low-Light Image Enhancement: A Benchmark and Transformer-Based MethodCode2
Unsupervised Night Image Enhancement: When Layer Decomposition Meets Light-Effects SuppressionCode2
URetinex-Net: Retinex-Based Deep Unfolding Network for Low-Light Image EnhancementCode2
AdaInt: Learning Adaptive Intervals for 3D Lookup Tables on Real-time Image EnhancementCode2
Learning Semantic-Aware Knowledge Guidance for Low-Light Image EnhancementCode2
Low-Light Image Enhancement via Structure Modeling and GuidanceCode2
You Only Need 90K Parameters to Adapt Light: A Light Weight Transformer for Image Enhancement and Exposure CorrectionCode2
GLARE: Low Light Image Enhancement via Generative Latent Feature based Codebook RetrievalCode2
Bayesian Enhancement Models for One-to-Many Mapping in Image EnhancementCode2
AllWeatherNet:Unified Image Enhancement for Autonomous Driving under Adverse Weather and Lowlight-conditionsCode2
Fast Context-Based Low-Light Image Enhancement via Neural Implicit RepresentationsCode2
Bayesian Neural Networks for One-to-Many Mapping in Image EnhancementCode2
FlowIE: Efficient Image Enhancement via Rectified FlowCode2
CRNet: A Detail-Preserving Network for Unified Image Restoration and Enhancement TaskCode2
IRSRMamba: Infrared Image Super-Resolution via Mamba-based Wavelet Transform Feature Modulation ModelCode2
Learning to See in the Extremely DarkCode2
Adaptive Dual-domain Learning for Underwater Image EnhancementCode2
Degradation-Aware Feature Perturbation for All-in-One Image RestorationCode2
Low-Light Image Enhancement with Wavelet-based Diffusion ModelsCode2
Color Shift Estimation-and-Correction for Image EnhancementCode2
Misalignment-Robust Frequency Distribution Loss for Image TransformationCode2
Wave-Mamba: Wavelet State Space Model for Ultra-High-Definition Low-Light Image EnhancementCode2
DC-ShadowNet: Single-Image Hard and Soft Shadow Removal Using Unsupervised Domain-Classifier Guided NetworkCode2
Flow Matching for Medical Image Synthesis: Bridging the Gap Between Speed and QualityCode2
Implicit Neural Representation for Cooperative Low-light Image EnhancementCode2
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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