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 726–750 of 983 papers

TitleStatusHype
DILIE: Deep Internal Learning for Image Enhancement—0
A Generative Approach for Detection-driven Underwater Image Enhancement—0
Retinex-inspired Unrolling with Cooperative Prior Architecture Search for Low-light Image EnhancementCode1
Sylvester Matrix Based Similarity Estimation Method for Automation of Defect Detection in Textile Fabrics—0
Raw Image DeblurringCode1
NICER: Aesthetic Image Enhancement with Humans in the LoopCode1
Exploring the Effect of Image Enhancement Techniques on COVID-19 Detection using Chest X-rays ImagesCode1
Legacy Photo Editing with Learned Noise PriorCode1
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
Blind Video Temporal Consistency via Deep Video PriorCode1
Two-stage generative adversarial networks for document image binarization with color noise and background removalCode1
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
Learning Image-adaptive 3D Lookup Tables for High Performance Photo Enhancement in Real-timeCode1
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
Retaining Image Feature Matching Performance Under Low Light Conditions—0
When Image Decomposition Meets Deep Learning: A Novel Infrared and Visible Image Fusion Method—0
Noise-Aware Texture-Preserving Low-Light Enhancement—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