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 801–825 of 983 papers

TitleStatusHype
Visual Perception Model for Rapid and Adaptive Low-light Image Enhancement—0
Flexible Example-based Image Enhancement with Task Adaptive Global Feature Self-Guided Network—0
Encoding in the Dark Grand Challenge: An Overview—0
Unsupervised Low-light Image Enhancement with Decoupled Networks—0
Reconstruction of high-resolution 6x6-mm OCT angiograms using deep learning—0
Superkernel Neural Architecture Search for Image Denoising—0
Underwater image enhancement with Image Colorfulness Measure—0
On Box-Cox Transformation for Image Normality and Pattern Classification—0
Underwater Image Enhancement Based on Structure-Texture Reconstruction—0
Harmony-Search and Otsu based System for Coronavirus Disease (COVID-19) Detection using Lung CT Scan ImagesCode0
Super Resolution for Root ImagingCode0
Medical Image Enhancement Using Histogram Processing and Feature Extraction for Cancer Classification—0
Domain Adaptive Adversarial Learning Based on Physics Model Feedback for Underwater Image Enhancement—0
SIP-SegNet: A Deep Convolutional Encoder-Decoder Network for Joint Semantic Segmentation and Extraction of Sclera, Iris and Pupil based on Periocular Region Suppression—0
Perception and Navigation in Autonomous Systems in the Era of Learning: A Survey—0
Supervised and Unsupervised Learning of Parameterized Color Enhancement—0
Self-supervised Fine-tuning for Correcting Super-Resolution Convolutional Neural Networks—0
Image Enhanced Rotation Prediction for Self-Supervised Learning—0
Σ-net: Systematic Evaluation of Iterative Deep Neural Networks for Fast Parallel MR Image ReconstructionCode0
Unpaired Image Enhancement Featuring Reinforcement-Learning-Controlled Image Editing Software—0
A hierarchical approach to deep learning and its application to tomographic reconstruction—0
Adaptive GNN for Image Analysis and Editing—0
CURL: Neural Curve Layers for Global Image EnhancementCode0
Progressive Retinex: Mutually Reinforced Illumination-Noise Perception Network for Low Light Image Enhancement—0
RIS-GAN: Explore Residual and Illumination with Generative Adversarial Networks for Shadow RemovalCode0
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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