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 101–125 of 983 papers

TitleStatusHype
Zero-Shot Low Light Image Enhancement with Diffusion Prior—0
Leveraging Content and Context Cues for Low-Light Image EnhancementCode0
Deep Joint Unrolling for Deblurring and Low-Light Image Enhancement (JUDE)—0
Analytical-Heuristic Modeling and Optimization for Low-Light Image Enhancement—0
Phaseformer: Phase-based Attention Mechanism for Underwater Image Restoration and BeyondCode1
MambaNUT: Nighttime UAV Tracking via Mamba-based Adaptive Curriculum LearningCode1
DMFourLLIE: Dual-Stage and Multi-Branch Fourier Network for Low-Light Image EnhancementCode1
Self-Supervised Denoiser Framework—0
HUPE: Heuristic Underwater Perceptual Enhancement with Semantic Collaborative LearningCode1
MambaTrack: Exploiting Dual-Enhancement for Night UAV TrackingCode0
Beneath the Surface: The Role of Underwater Image Enhancement in Object DetectionCode0
Zero-Shot Low-Light Image Enhancement via Joint Frequency Domain Priors Guided DiffusionCode1
Contourlet Refinement Gate Framework for Thermal Spectrum Distribution Regularized Infrared Image Super-ResolutionCode2
Oscillation Inversion: Understand the structure of Large Flow Model through the Lens of Inversion Method—0
Underwater Image Enhancement with Cascaded Contrastive LearningCode1
Dropout the High-rate Downsampling: A Novel Design Paradigm for UHD Image Restoration—0
Enhancing Low-Light Images with Kolmogorov–Arnold Networks in Transformer AttentionCode0
ERUP-YOLO: Enhancing Object Detection Robustness for Adverse Weather Condition by Unified Image-Adaptive Processing—0
TPOT: Topology Preserving Optimal Transport in Retinal Fundus Image EnhancementCode0
Medical X-Ray Image Enhancement Using Global Contrast-Limited Adaptive Histogram Equalization—0
High-Fidelity Document Stain Removal via A Large-Scale Real-World Dataset and A Memory-Augmented TransformerCode1
Analyzing Noise Models and Advanced Filtering Algorithms for Image EnhancementCode0
Wavelet-based Mamba with Fourier Adjustment for Low-light Image EnhancementCode2
Deep Learning, Machine Learning -- Digital Signal and Image Processing: From Theory to Application—0
HUE Dataset: High-Resolution Event and Frame Sequences for Low-Light Vision—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