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 751–775 of 983 papers

TitleStatusHype
Low Light Image Enhancement via Global and Local Context Modeling—0
A Switched View of Retinex: Deep Self-Regularized Low-Light Image Enhancement—0
Perceptual Variousness Motion Deblurring With Light Global Context RefinementCode0
Deep Symmetric Network for Underexposed Image Enhancement With Recurrent Attentional Learning—0
FGF-GAN: A Lightweight Generative Adversarial Network for Pansharpening via Fast Guided Filter—0
Unpaired Image Enhancement with Quality-Attention Generative Adversarial Network—0
UMLE: Unsupervised Multi-discriminator Network for Low Light Enhancement—0
LEUGAN:Low-Light Image Enhancement by Unsupervised Generative Attentional Networks—0
SID-NISM: A Self-supervised Low-light Image Enhancement Framework—0
Projected Distribution Loss for Image EnhancementCode0
DILIE: Deep Internal Learning for Image Enhancement—0
A Generative Approach for Detection-driven Underwater Image Enhancement—0
Sylvester Matrix Based Similarity Estimation Method for Automation of Defect Detection in Textile Fabrics—0
Privacy-Preserving Pose Estimation for Human-Robot InteractionCode0
Application of Compromising Evolution in Multi-objective Image Error Concealment—0
Chest X-ray Image Phase Features for Improved Diagnosis of COVID-19 Using Convolutional Neural NetworkCode0
A Two-stage Unsupervised Approach for Low light Image Enhancement—0
Photovoltaic module segmentation and thermal analysis tool from thermal images—0
Checkerboard-Artifact-Free Image-Enhancement Network Considering Local and Global Features—0
Neural Enhancement in Content Delivery Systems: The State-of-the-Art and Future Directions—0
An approach for underwater image enhancement based on color correction and dehazing—0
A Generative Adversarial Approach with Residual Learning for Dust and Scratches Artifacts Removal—0
MFIF-GAN: A New Generative Adversarial Network for Multi-Focus Image Fusion—0
Blind Image Restoration with Flow Based Priors—0
When Image Decomposition Meets Deep Learning: A Novel Infrared and Visible Image Fusion Method—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