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 601–625 of 983 papers

TitleStatusHype
Visual Recognition-Driven Image Restoration for Multiple Degradation With Intrinsic Semantics Recovery—0
SALVE: Self-supervised Adaptive Low-light Video Enhancement—0
Low-Light Image and Video Enhancement: A Comprehensive Survey and BeyondCode0
WavEnhancer: Unifying Wavelet and Transformer for Image Enhancement—0
Dynamic Test-Time Augmentation via Differentiable FunctionsCode0
On the Robustness of Normalizing Flows for Inverse Problems in Imaging—0
Day2Dark: Pseudo-Supervised Activity Recognition beyond Silent Daylight—0
Mutual Guidance and Residual Integration for Image Enhancement—0
SLLEN: Semantic-aware Low-light Image Enhancement Network—0
Learning to Kindle the StarlightCode0
Noise Self-Regression: A New Learning Paradigm to Enhance Low-Light Images Without Task-Related Data—0
Deep Learning for Image Enhancement and Correction in Magnetic Resonance Imaging—State-of-the-Art and Challenges—0
Decoupled Cross-Scale Cross-View Interaction for Stereo Image Enhancement in The Dark—0
Multi-Scale Structural-aware Exposure Correction for Endoscopic Imaging—0
Perceptual Image Enhancement for Smartphone Real-Time Applications—0
Boomerang: Local sampling on image manifolds using diffusion models—0
Deformably-Scaled Transposed Convolution—0
Retinex Image Enhancement Based on Sequential Decomposition With a Plug-and-Play Framework—0
Seeing Through the Noisy Dark: Towards Real-world Low-Light Image Enhancement and Denoising—0
Deep Perceptual Enhancement for Medical Image AnalysisCode0
Distilling Style from Image Pairs for Global Forward and Inverse Tone MappingCode0
DPFNet: A Dual-branch Dilated Network with Phase-aware Fourier Convolution for Low-light Image EnhancementCode0
DEANet: Decomposition Enhancement and Adjustment Network for Low-Light Image Enhancement—0
Reflectance-Oriented Probabilistic Equalization for Image EnhancementCode0
Realistic Hair Synthesis with Generative Adversarial Networks—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