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 501–525 of 983 papers

TitleStatusHype
Decoupled Cross-Scale Cross-View Interaction for Stereo Image Enhancement in The Dark—0
Multi-Scale Structural-aware Exposure Correction for Endoscopic Imaging—0
Local Color Distributions Prior for Image EnhancementCode1
Perceptual Image Enhancement for Smartphone Real-Time Applications—0
Boomerang: Local sampling on image manifolds using diffusion models—0
Perceptual Multi-Exposure FusionCode1
Degradation-invariant Enhancement of Fundus Images via Pyramid Constraint NetworkCode1
Deformably-Scaled Transposed Convolution—0
Retinex Image Enhancement Based on Sequential Decomposition With a Plug-and-Play Framework—0
Structure Representation Network and Uncertainty Feedback Learning for Dense Non-Uniform Fog RemovalCode1
PSENet: Progressive Self-Enhancement Network for Unsupervised Extreme-Light Image EnhancementCode1
Seeing Through the Noisy Dark: Towards Real-world Low-Light Image Enhancement and Denoising—0
Deep Perceptual Enhancement for Medical Image AnalysisCode0
Distilling Style from Image Pairs for Global Forward and Inverse Tone MappingCode0
GDIP: Gated Differentiable Image Processing for Object-Detection in Adverse ConditionsCode1
DPFNet: A Dual-branch Dilated Network with Phase-aware Fourier Convolution for Low-light Image EnhancementCode0
Reflectance-Oriented Probabilistic Equalization for Image EnhancementCode0
DEANet: Decomposition Enhancement and Adjustment Network for Low-Light Image Enhancement—0
Reflectance-Guided, Contrast-Accumulated Histogram EqualizationCode1
Realistic Hair Synthesis with Generative Adversarial Networks—0
USLN: A statistically guided lightweight network for underwater image enhancement via dual-statistic white balance and multi-color space stretchCode1
4D LUT: Learnable Context-Aware 4D Lookup Table for Image Enhancement—0
VEViD: Vision Enhancement via Virtual diffraction and coherent DetectionCode2
2nd Place Solutions for UG2+ Challenge 2022 -- D^3Net for Mitigating Atmospheric Turbulence from Images—0
Low-Light Video Enhancement with Synthetic Event Guidance—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