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 826–850 of 983 papers

TitleStatusHype
Striving for Faster and Better: A One-Layer Architecture with Auto Re-parameterization for Low-Light Image EnhancementCode0
X-CAUNET: Cross-Color Channel Attention with Underwater Image-Enhancing TransformerCode0
PDCFNet: Enhancing Underwater Images through Pixel Difference ConvolutionCode0
Enhance to Read Better: A Multi-Task Adversarial Network for Handwritten Document Image EnhancementCode0
Low-light Image Enhancement Using the Cell Vibration ModelCode0
SNeurodCNN: Structure-focused Neurodegeneration Convolutional Neural Network for Modelling and Classification of Alzheimer's DiseaseCode0
Multiscale Coarse-to-Fine Guided Screenshot DemoiréingCode0
An Experimental-based Review of Image Enhancement and Image Restoration Methods for Underwater ImagingCode0
Multi-image Tranformer for Multi-focus Image FusionCode0
Perceptual Variousness Motion Deblurring With Light Global Context RefinementCode0
Empowering Resampling Operation for Ultra-High-Definition Image Enhancement with Model-Aware GuidanceCode0
Style Generator Inversion for Image Enhancement and AnimationCode0
Underwater Image Enhancement by Convolutional Spiking Neural NetworksCode0
Context-Aware Optimal Transport Learning for Retinal Fundus Image EnhancementCode0
Personalized Image Enhancement Featuring Masked Style ModelingCode0
MSFNet-CPD: Multi-Scale Cross-Modal Fusion Network for Crop Pest DetectionCode0
Super Resolution for Root ImagingCode0
Context-Aware Automatic Occlusion RemovalCode0
SurroundNet: Towards Effective Low-Light Image EnhancementCode0
PHISWID: Physics-Inspired Underwater Image Dataset Synthesized from RGB-D ImagesCode0
Mop Moire Patterns Using MopNetCode0
XNet: A convolutional neural network (CNN) implementation for medical X-Ray image segmentation suitable for small datasetsCode0
Moiré Photo Restoration Using Multiresolution Convolutional Neural NetworksCode0
MixNet: Efficient Global Modeling for Ultra-High-Definition Image RestorationCode0
Plug-and-Play gradient-based denoisers applied to CT 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
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