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
Enhancing Low-Light Images Using Infrared-Encoded Images—0
Face Image Quality Enhancement Study for Face Recognition—0
Joint Perceptual Learning for Enhancement and Object Detection in Underwater Scenarios—0
UIT-Saviors at MEDVQA-GI 2023: Improving Multimodal Learning with Image Enhancement for Gastrointestinal Visual Question Answering—0
Learning to Augment: Hallucinating Data for Domain Generalized Segmentation—0
Low-Light Enhancement in the Frequency Domain—0
A ground-based dataset and a diffusion model for on-orbit low-light image enhancement—0
CAMP-Net: Consistency-Aware Multi-Prior Network for Accelerated MRI ReconstructionCode0
Optical Coherence Tomography Image Enhancement via Block Hankelization and Low Rank Tensor Network Approximation—0
PUGAN: Physical Model-Guided Underwater Image Enhancement Using GAN with Dual-DiscriminatorsCode0
Personalized Image Enhancement Featuring Masked Style ModelingCode0
UIERL: Internal-External Representation Learning Network for Underwater Image EnhancementCode0
Investigation of the Challenges of Underwater-Visual-Monocular-SLAM—0
LUT-GCE: Lookup Table Global Curve Estimation for Fast Low-light Image Enhancement—0
RB-Dust -- A Reference-based Dataset for Vision-based Dust Removal—0
Unsupervised Low Light Image Enhancement Using SNR-Aware Swin Transformer—0
VEDA: Uneven light image enhancement via a vision-based exploratory data analysis modelCode0
Make Lossy Compression Meaningful for Low-Light ImagesCode0
Learning a Single Convolutional Layer Model for Low Light Image Enhancement—0
Bright Channel Prior Attention for Multispectral Pedestrian Detection—0
Advancing Unsupervised Low-light Image Enhancement: Noise Estimation, Illumination Interpolation, and Self-RegulationCode0
SCRNet: a Retinex Structure-based Low-light Enhancement Model Guided by Spatial Consistency—0
Pyramid Texture Filtering—0
Hybrid Transformer and CNN Attention Network for Stereo Image Super-resolution—0
Venn Diagram Multi-label Class Interpretation of Diabetic Foot Ulcer with Color and Sharpness Enhancement—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