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 876–900 of 983 papers

TitleStatusHype
NightHaze: Nighttime Image Dehazing via Self-Prior Learning—0
NLHD: A Pixel-Level Non-Local Retinex Model for Low-Light Image Enhancement—0
Noise-Aware Texture-Preserving Low-Light Enhancement—0
Noise Self-Regression: A New Learning Paradigm to Enhance Low-Light Images Without Task-Related Data—0
Nonlinear Filter Based Image Denoising Using AMF Approach—0
Nonlocal Retinex-Based Variational Model and its Deep Unfolding Twin for Low-Light Image Enhancement—0
On-board classification of underwater images using hybrid classical-quantum CNN based method—0
On Box-Cox Transformation for Image Normality and Pattern Classification—0
On the Duality Between Retinex and Image Dehazing—0
On the limits of perceptual quality measures for enhanced underwater images—0
On the Robustness of Normalizing Flows for Inverse Problems in Imaging—0
Optical Coherence Tomography Image Enhancement via Block Hankelization and Low Rank Tensor Network Approximation—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
Performance Evaluation of Infrared Image Enhancement Techniques—0
Photo-unrealistic Image Enhancement for Subject Placement in Outdoor Photography—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
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