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 451–475 of 983 papers

TitleStatusHype
Human Guided Ground-truth Generation for Realistic Image Super-resolutionCode1
Low-Light Image Enhancement by Learning Contrastive Representations in Spatial and Frequency Domains—0
Implicit Neural Representation for Cooperative Low-light Image EnhancementCode2
Denoising Diffusion Post-Processing for Low-Light Image EnhancementCode1
Image Enhancement for Remote Photoplethysmography in a Low-Light EnvironmentCode0
Joint Multi-Scale Tone Mapping and Denoising for HDR Image EnhancementCode0
RSFNet: A White-Box Image Retouching Approach using Region-Specific Color FiltersCode1
MetaUE: Model-based Meta-learning for Underwater Image EnhancementCode0
Retinexformer: One-stage Retinex-based Transformer for Low-light Image EnhancementCode5
Learning Enhancement From Degradation: A Diffusion Model For Fundus Image EnhancementCode1
Retinal Image Restoration using Transformer and Cycle-Consistent Generative Adversarial NetworkCode1
CUR Transformer: A Convolutional Unbiased Regional Transformer for Image DenoisingCode0
RSFDM-Net: Real-time Spatial and Frequency Domains Modulation Network for Underwater Image Enhancement—0
Embedding Fourier for Ultra-High-Definition Low-Light Image Enhancement—0
Bridging Synthetic and Real Images: a Transferable and Multiple Consistency aided Fundus Image Enhancement Framework—0
Gap-closing Matters: Perceptual Quality Evaluation and Optimization of Low-Light Image EnhancementCode1
SyreaNet: A Physically Guided Underwater Image Enhancement Framework Integrating Synthetic and Real ImagesCode1
Effective Document Image Enhancement Using tokens-to-token Transformer NetworkCode1
A Correction-Based Dynamic Enhancement Framework towards Underwater Detection—0
OTRE: Where Optimal Transport Guided Unpaired Image-to-Image Translation Meets Regularization by EnhancingCode1
GDB: Gated convolutions-based Document BinarizationCode1
UW-CVGAN: UnderWater Image Enhancement with Capsules Vectors Quantization—0
STEPS: Joint Self-supervised Nighttime Image Enhancement and Depth EstimationCode1
Rethinking low-cost microscopy workflow: Image enhancement using deep based Extended Depth of Field methodsCode1
Self-FuseNet: Data Free Unsupervised Remote Sensing Image Super-Resolution—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