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 376–400 of 983 papers

TitleStatusHype
Influence of image noise on crack detection performance of deep convolutional neural networks—0
Information-Theoretic GAN Compression with Variational Energy-based Model—0
Iris Biometric System using a hybrid approach—0
Language Independent Single Document Image Super-Resolution using CNN for improved recognition—0
GIQE: Generic Image Quality Enhancement via Nth Order Iterative Degradation—0
Convolutional Neural Pyramid for Image Processing—0
Generalized Task-Driven Medical Image Quality Enhancement with Gradient Promotion—0
A Fusion Adversarial Underwater Image Enhancement Network with a Public Test Dataset—0
Convolutional Neural Networks Considering Local and Global features for Image Enhancement—0
Application of Visual Communication in Image Enhancement and Optimization of Human–Computer Interface—0
Controllable Image Enhancement—0
GM-MoE: Low-Light Enhancement with Gated-Mechanism Mixture-of-Experts—0
Cross-Domain Underwater Image Enhancement Guided by No-Reference Image Quality Assessment: A Transfer Learning Approach—0
Going the Extra Mile in Face Image Quality Assessment: A Novel Database and Model—0
Gradient-Based Low-Light Image Enhancement—0
Image Inpainting by Adaptive Fusion of Variable Spline Interpolations—0
Gray level image enhancement using the Bernstein polynomials—0
Image Restoration in Non-Linear Filtering Domain using MDB approach—0
Guided Colorization Using Mono-Color Image Pairs—0
FusionNet: Multi-model Linear Fusion Framework for Low-light Image Enhancement—0
Contrast Enhancement of Medical X-Ray Image Using Morphological Operators with Optimal Structuring Element—0
CURVE: CLIP-Utilized Reinforcement Learning for Visual Image Enhancement via Simple Image Processing—0
A Retinex based GAN Pipeline to Utilize Paired and Unpaired Datasets for Enhancing Low Light Images—0
From Fidelity to Perceptual Quality: A Semi-Supervised Approach for Low-Light Image Enhancement—0
Contrast Enhancement And Brightness Preservation Using Multi- Decomposition Histogram Equalization—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