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 276–300 of 983 papers

TitleStatusHype
Abandoning the Bayer-Filter to See in the DarkCode1
NamedCurves: Learned Image Enhancement via Color NamingCode1
Double Domain Guided Real-Time Low-Light Image Enhancement for Ultra-High-Definition Transportation SurveillanceCode1
Joint Correcting and Refinement for Balanced Low-Light Image EnhancementCode1
DPF-Net: Physical Imaging Model Embedded Data-Driven Underwater Image EnhancementCode1
KAN See In the DarkCode1
Learn from Unpaired Data for Image Restoration: A Variational Bayes ApproachCode1
Self-supervised Low Light Image Enhancement and DenoisingCode1
Learning a Simple Low-Light Image Enhancer From Paired Low-Light InstancesCode1
Learning Enhancement From Degradation: A Diffusion Model For Fundus Image EnhancementCode1
Learning Enriched Features for Real Image Restoration and EnhancementCode1
ALL-E: Aesthetics-guided Low-light Image Enhancement—0
Dictionary learning based image enhancement for rarity detection—0
Multimodal Super-Resolution: Discovering hidden physics and its application to fusion plasmas—0
BiCo-Fusion: Bidirectional Complementary LiDAR-Camera Fusion for Semantic- and Spatial-Aware 3D Object Detection—0
Forensic Video Analytic Software—0
DGNet: Dynamic Gradient-Guided Network for Water-Related Optics Image Enhancement—0
DFR-TSD: A Deep Learning Based Framework for Robust Traffic Sign Detection Under Challenging Weather Conditions—0
Perceptual underwater image enhancement with deep learning and physical priors—0
BG-YOLO: A Bidirectional-Guided Method for Underwater Object Detection—0
Detection of Parasitic Eggs from Microscopy Images and the emergence of a new dataset—0
Design of Novel Algorithm and Architecture for Gaussian Based Color Image Enhancement System for Real Time Applications—0
Depth Image Enhancement Using Local Tangent Plane Approximations—0
Dense residual Transformer for image denoising—0
A Wavelet-based Dual-stream Network for Underwater Image 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
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