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 476–500 of 983 papers

TitleStatusHype
A Large-scale Film Style Dataset for Learning Multi-frequency Driven Film EnhancementCode1
Soft Thresholding for Visual Image Enhancement—0
Self-supervised Domain Adaptation for Breaking the Limits of Low-quality Fundus Image Quality Enhancement—0
DarkVision: A Benchmark for Low-light Image/Video Perception—0
Low-Light Image Enhancement with Multi-Stage Residue Quantization and Brightness-Aware AttentionCode1
Visual Recognition-Driven Image Restoration for Multiple Degradation With Intrinsic Semantics Recovery—0
Learning a Simple Low-Light Image Enhancer From Paired Low-Light InstancesCode1
DNF: Decouple and Feedback Network for Seeing in the DarkCode1
You Do Not Need Additional Priors or Regularizers in Retinex-Based Low-Light Image Enhancement—0
Ultra-High-Definition Low-Light Image Enhancement: A Benchmark and Transformer-Based MethodCode2
SALVE: Self-supervised Adaptive Low-light Video Enhancement—0
Low-Light Image and Video Enhancement: A Comprehensive Survey and BeyondCode0
Adaptive deep learning framework for robust unsupervised underwater image enhancementCode1
WavEnhancer: Unifying Wavelet and Transformer for Image Enhancement—0
Dynamic Test-Time Augmentation via Differentiable FunctionsCode0
On the Robustness of Normalizing Flows for Inverse Problems in Imaging—0
Day2Dark: Pseudo-Supervised Activity Recognition beyond Silent Daylight—0
Three-stage binarization of color document images based on discrete wavelet transform and generative adversarial networksCode1
Mutual Guidance and Residual Integration for Image Enhancement—0
SLLEN: Semantic-aware Low-light Image Enhancement Network—0
Semantic-aware Texture-Structure Feature Collaboration for Underwater Image EnhancementCode1
DGD-cGAN: A Dual Generator for Image Dewatering and RestorationCode1
Learning to Kindle the StarlightCode0
Noise Self-Regression: A New Learning Paradigm to Enhance Low-Light Images Without Task-Related Data—0
Deep Learning for Image Enhancement and Correction in Magnetic Resonance Imaging—State-of-the-Art and Challenges—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
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