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 526–550 of 983 papers

TitleStatusHype
Feature Enhancer Segmentation Network (FES-Net) for Vessel Segmentation—0
CARNet: Collaborative Adversarial Resilience for Robust Underwater Image Enhancement and Perception—0
Feature Attention Network (FA-Net): A Deep-Learning Based Approach for Underwater Single Image Enhancement—0
Low-light image enhancement based on sharpening-smoothing image filterCode0
IndGIC: Supervised Action Recognition under Low Illumination—0
Diff-Retinex: Rethinking Low-light Image Enhancement with A Generative Diffusion Model—0
DiffLLE: Diffusion-guided Domain Calibration for Unsupervised Low-light Image Enhancement—0
Application of Visual Communication in Image Enhancement and Optimization of Human–Computer Interface—0
Low-Light Image Enhancement with Illumination-Aware Gamma Correction and Complete Image Modelling Network—0
Geometry of the Visual Cortex with Applications to Image Inpainting and EnhancementCode0
TrainFors: A Large Benchmark Training Dataset for Image Manipulation Detection and Localization—0
Transmission and Color-guided Network for Underwater Image Enhancement—0
Brighten-and-Colorize: A Decoupled Network for Customized Low-Light Image Enhancement—0
Dual Degradation-Inspired Deep Unfolding Network for Low-Light Image Enhancement—0
WaterFlow: Heuristic Normalizing Flow for Underwater Image Enhancement and Beyond—0
Space Debris: Are Deep Learning-based Image Enhancements part of the Solution?—0
Decomposition Ascribed Synergistic Learning for Unified Image Restoration—0
Semantic Image Completion and Enhancement using GANs—0
Fast Dust Sand Image Enhancement Based on Color Correction and New Membership Function—0
Semi-supervised Underwater Image Enhancement Using A Physics-Aware Triple-Stream Network—0
Towards Authentic Face Restoration with Iterative Diffusion Models and Beyond—0
Division Gets Better: Learning Brightness-Aware and Detail-Sensitive Representations for Low-Light Image Enhancement—0
Multiscale Coarse-to-Fine Guided Screenshot DemoiréingCode0
HQG-Net: Unpaired Medical Image Enhancement with High-Quality Guidance—0
Bio-Inspired Night Image Enhancement Based on Contrast Enhancement and Denoising—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