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 1–25 of 983 papers

TitleStatusHype
HVI-CIDNet+: Beyond Extreme Darkness for Low-Light Image EnhancementCode1
MAC-Lookup: Multi-Axis Conditional Lookup Model for Underwater Image EnhancementCode0
Learning to See in the Extremely DarkCode2
TDiR: Transformer based Diffusion for Image Restoration Tasks—0
A Multi-Scale Spatial Attention-Based Zero-Shot Learning Framework for Low-Light Image Enhancement—0
Temperature calibration of surface emissivities with an improved thermal image enhancement network—0
DREAM: On hallucinations in AI-generated content for nuclear medicine imaging—0
Exposure-slot: Exposure-centric representations learning with Slot-in-Slot Attention for Region-aware Exposure CorrectionCode1
Physics Informed Capsule Enhanced Variational AutoEncoder for Underwater Image Enhancement—0
A Poisson-Guided Decomposition Network for Extreme Low-Light Image Enhancement—0
RT-X Net: RGB-Thermal cross attention network for Low-Light Image EnhancementCode1
CURVE: CLIP-Utilized Reinforcement Learning for Visual Image Enhancement via Simple Image Processing—0
URWKV: Unified RWKV Model with Multi-state Perspective for Low-light Image RestorationCode1
See through the Dark: Learning Illumination-affined Representations for Nighttime Occupancy PredictionCode1
Underwater Diffusion Attention Network with Contrastive Language-Image Joint Learning for Underwater Image Enhancement—0
Improving Novel view synthesis of 360^ Scenes in Extremely Sparse Views by Jointly Training Hemisphere Sampled Synthetic ImagesCode0
Degradation-Aware Feature Perturbation for All-in-One Image RestorationCode2
Entropy-Driven Genetic Optimization for Deep-Feature-Guided Low-Light Image EnhancementCode0
Super-Resolution Generative Adversarial Networks based Video Enhancement—0
PDE: Gene Effect Inspired Parameter Dynamic Evolution for Low-light Image Enhancement—0
A review of advancements in low-light image enhancement using deep learning—0
ADNP-15: An Open-Source Histopathological Dataset for Neuritic Plaque Segmentation in Human Brain Whole Slide Images with Frequency Domain Image Enhancement for Stain Normalization—0
TS-Diff: Two-Stage Diffusion Model for Low-Light RAW Image EnhancementCode1
MSFNet-CPD: Multi-Scale Cross-Modal Fusion Network for Crop Pest DetectionCode0
HiLLIE: Human-in-the-Loop Training for Low-Light Image EnhancementCode0
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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