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 351–375 of 983 papers

TitleStatusHype
Improving Deep Learning-based Defect Detection on Window Frames with Image Processing Strategies—0
UnitModule: A Lightweight Joint Image Enhancement Module for Underwater Object Detection—0
Toward Sufficient Spatial-Frequency Interaction for Gradient-aware Underwater Image EnhancementCode1
Feature Enhancer Segmentation Network (FES-Net) for Vessel Segmentation—0
Underwater Image Enhancement by Transformer-based Diffusion Model with Non-uniform Sampling for Skip StrategyCode1
Empowering Low-Light Image Enhancer through Customized Learnable PriorsCode1
CARNet: Collaborative Adversarial Resilience for Robust Underwater Image Enhancement and Perception—0
A Generic Fundus Image Enhancement Network Boosted by Frequency Self-supervised Representation LearningCode1
Multiple transformation function estimation for image enhancementCode1
Improving Underwater Visual Tracking With a Large Scale Dataset and Image EnhancementCode1
Feature Attention Network (FA-Net): A Deep-Learning Based Approach for Underwater Single Image Enhancement—0
Low-light image enhancement based on sharpening-smoothing image filterCode0
IndGIC: Supervised Action Recognition under Low Illumination—0
Pixel-Aware Stable Diffusion for Realistic Image Super-resolution and Personalized StylizationCode3
Generalized Lightness Adaptation with Channel Selective NormalizationCode1
Diff-Retinex: Rethinking Low-light Image Enhancement with A Generative Diffusion Model—0
CDAN: Convolutional dense attention-guided network for low-light image enhancementCode1
Synergistic Multiscale Detail Refinement via Intrinsic Supervision for Underwater Image EnhancementCode1
DiffLLE: Diffusion-guided Domain Calibration for Unsupervised Low-light Image Enhancement—0
Application of Visual Communication in Image Enhancement and Optimization of Human–Computer Interface—0
Self-Reference Deep Adaptive Curve Estimation for Low-Light Image EnhancementCode1
Low-Light Image Enhancement with Illumination-Aware Gamma Correction and Complete Image Modelling Network—0
Geometry of the Visual Cortex with Applications to Image Inpainting and EnhancementCode0
A Generalized Physical-knowledge-guided Dynamic Model for Underwater Image EnhancementCode1
TrainFors: A Large Benchmark Training Dataset for Image Manipulation Detection and Localization—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