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 551–575 of 983 papers

TitleStatusHype
Controllable Image Enhancement—0
Luminance-Guided Chrominance Image Enhancement for HEVC Intra CodingCode0
Self-Supervised Low-Light Image Enhancement Using Discrepant Untrained Network PriorsCode1
Structure-consistent Restoration Network for Cataract Fundus Image EnhancementCode1
Poisson2Sparse: Self-Supervised Poisson Denoising From a Single ImageCode1
You Only Need 90K Parameters to Adapt Light: A Light Weight Transformer for Image Enhancement and Exposure CorrectionCode2
TreEnhance: A Tree Search Method For Low-Light Image EnhancementCode1
A Novel Underwater Image Enhancement and Improved Underwater Biological Detection Pipeline—0
UIF: An Objective Quality Assessment for Underwater Image Enhancement—0
Towards Robust Low Light Image Enhancement—0
Dense residual Transformer for image denoising—0
Lightweight Image Enhancement Network for Mobile Devices Using Self-Feature Extraction and Dense Modulation—0
Reinforced Swin-Convs Transformer for Underwater Image EnhancementCode1
AdaInt: Learning Adaptive Intervals for 3D Lookup Tables on Real-time Image EnhancementCode2
Two Decades of Colorization and Decolorization for Images and Videos—0
Exposure Correction Model to Enhance Image QualityCode1
Toward Fast, Flexible, and Robust Low-Light Image EnhancementCode2
Learn from Unpaired Data for Image Restoration: A Variational Bayes ApproachCode1
Image Restoration in Non-Linear Filtering Domain using MDB approach—0
MultiPathGAN: Structure Preserving Stain Normalization using Unsupervised Multi-domain Adversarial Network with Perception LossCode1
Learning Enriched Features for Fast Image Restoration and Enhancement—0
UID2021: An Underwater Image Dataset for Evaluation of No-reference Quality Assessment MetricsCode0
Underwater Image Enhancement Using Pre-trained Transformer—0
Lightweight HDR Camera ISP for Robust Perception in Dynamic Illumination Conditions via Fourier Adversarial Networks—0
Extremely Low-light Image Enhancement with Scene Text RestorationCode0
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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