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 626–650 of 983 papers

TitleStatusHype
K-means segmentation based-on lab color space for embryo detection in incubated egg—0
L2BGAN: An image enhancement model for image quality improvement and image analysis tasks without paired supervision—0
LAFFNet: A Lightweight Adaptive Feature Fusion Network for Underwater Image Enhancement—0
Language Independent Single Document Image Super-Resolution using CNN for improved recognition—0
Latent Disentanglement for Low Light Image Enhancement—0
Learned Perceptual Image Enhancement—0
Learn From Orientation Prior for Radiograph Super-Resolution: Orientation Operator Transformer—0
Learning Aggregated Transmission Propagation Networks for Haze Removal and Beyond—0
Learning a No-Reference Quality Assessment Model of Enhanced Images With Big Data—0
Learning a Single Convolutional Layer Model for Low Light Image Enhancement—0
Learning-Based Dequantization For Image Restoration Against Extremely Poor Illumination—0
Learning-based Image Enhancement for Visual Odometry in Challenging HDR Environments—0
Learning Color Representations for Low-Light Image Enhancement—0
Learning Dynamic Guidance for Depth Image Enhancement—0
Optimized Pap Smear Image Enhancement: Hybrid PMD Filter-CLAHE Using Spider Monkey Optimization—0
Oscillation Inversion: Understand the structure of Large Flow Model through the Lens of Inversion Method—0
Overparametrization of HyperNetworks at Fixed FLOP-Count Enables Fast Neural Image Enhancement—0
Palette-based Color Transfer between Images—0
Parametric Scaling of Preprocessing assisted U-net Architecture for Improvised Retinal Vessel Segmentation—0
PDE: Gene Effect Inspired Parameter Dynamic Evolution for Low-light Image Enhancement—0
Perception and Navigation in Autonomous Systems in the Era of Learning: A Survey—0
Perceptual Image Enhancement for Smartphone Real-Time Applications—0
Perfect Fingerprint Orientation Fields by Locally Adaptive Global Models—0
Performance Evaluation of Histogram Equalization and Fuzzy image Enhancement Techniques on Low Contrast Images—0
Performance Evaluation of Image Enhancement Techniques on Transfer Learning for Touchless Fingerprint Recognition—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