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 101–125 of 983 papers

TitleStatusHype
Degrade is Upgrade: Learning Degradation for Low-light Image EnhancementCode1
Denoising Diffusion Post-Processing for Low-Light Image EnhancementCode1
3D-StyleGAN: A Style-Based Generative Adversarial Network for Generative Modeling of Three-Dimensional Medical ImagesCode1
Diff-Mosaic: Augmenting Realistic Representations in Infrared Small Target Detection via Diffusion PriorCode1
A Generic Fundus Image Enhancement Network Boosted by Frequency Self-supervised Representation LearningCode1
Dimma: Semi-supervised Low Light Image Enhancement with Adaptive DimmingCode1
DocEnTr: An End-to-End Document Image Enhancement TransformerCode1
High-Fidelity Document Stain Removal via A Large-Scale Real-World Dataset and A Memory-Augmented TransformerCode1
A CNN-Based Blind Denoising Method for Endoscopic ImagesCode1
DMFourLLIE: Dual-Stage and Multi-Branch Fourier Network for Low-Light Image EnhancementCode1
A guided edge-aware smoothing-sharpening filter based on patch interpolation model and generalized Gamma distributionCode1
Double Domain Guided Real-Time Low-Light Image Enhancement for Ultra-High-Definition Transportation SurveillanceCode1
Few-Shot Domain Adaptation for Low Light RAW Image EnhancementCode1
ExposureDiffusion: Learning to Expose for Low-light Image EnhancementCode1
Exposure-slot: Exposure-centric representations learning with Slot-in-Slot Attention for Region-aware Exposure CorrectionCode1
Image-Adaptive YOLO for Object Detection in Adverse Weather ConditionsCode1
Adversarial Color Enhancement: Generating Unrestricted Adversarial Images by Optimizing a Color FilterCode1
Early Exit or Not: Resource-Efficient Blind Quality Enhancement for Compressed ImagesCode1
Five A^+ Network: You Only Need 9K Parameters for Underwater Image EnhancementCode1
GDIP: Gated Differentiable Image Processing for Object-Detection in Adverse ConditionsCode1
A New Benchmark In Vivo Paired Dataset for Laparoscopic Image De-smokingCode1
Exchange means change: an unsupervised single-temporal change detection framework based on intra- and inter-image patch exchangeCode1
EnlightenGAN: Deep Light Enhancement without Paired SupervisionCode1
Better Than Reference In Low Light Image Enhancement: Conditional Re-Enhancement NetworksCode1
Conditional Consistency Guided Image Translation and EnhancementCode1
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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
3AdaIntPSNR on proRGB25.49—Unverified
4RSFNet-mapPSNR 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