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 926–950 of 983 papers

TitleStatusHype
CURL: Neural Curve Layers for Global Image EnhancementCode0
HiLLIE: Human-in-the-Loop Training for Low-Light Image EnhancementCode0
Harnessing Multi-resolution and Multi-scale Attention for Underwater Image RestorationCode0
TPOT: Topology Preserving Optimal Transport in Retinal Fundus Image EnhancementCode0
Harmony-Search and Otsu based System for Coronavirus Disease (COVID-19) Detection using Lung CT Scan ImagesCode0
A Comprehensive Survey on Underwater Image Enhancement Based on Deep LearningCode0
ALEN: A Dual-Approach for Uniform and Non-Uniform Low-Light Image EnhancementCode0
VIIS: Visible and Infrared Information Synthesis for Severe Low-light Image EnhancementCode0
SDM-Car: A Dataset for Small and Dim Moving Vehicles Detection in Satellite VideosCode0
Zero-Shot Enhancement of Low-Light Image Based on Retinex DecompositionCode0
Attention based Broadly Self-guided Network for Low light Image EnhancementCode0
DHSGAN: An End to End Dehazing Network for Fog and SmokeCode0
Deep Underwater Image EnhancementCode0
DeepSPG: Exploring Deep Semantic Prior Guidance for Low-light Image Enhancement with Multimodal LearningCode0
GMT: Enhancing Generalizable Neural Rendering via Geometry-Driven Multi-Reference Texture TransferCode0
Unsharp Masking Layer: Injecting Prior Knowledge in Convolutional Networks for Image ClassificationCode0
Segmentation-Aware Generative Reinforcement Network (GRN) for Tissue Layer Segmentation in 3-D Ultrasound Images for Chronic Low-back Pain (cLBP) AssessmentCode0
Unsupervised Hyperspectral Stimulated Raman Microscopy Image Enhancement: Denoising and Segmentation via One-Shot Deep LearningCode0
Global and Local Enhancement Networks for Paired and Unpaired Image EnhancementCode0
Deep Retinex Decomposition for Low-Light EnhancementCode0
A Comprehensive Review of Image Enhancement TechniquesCode0
Deep Quantigraphic Image Enhancement via Comparametric EquationsCode0
Geometry of the Visual Cortex with Applications to Image Inpainting and EnhancementCode0
Generalizability analysis of deep learning predictions of human brain responses to augmented and semantically novel visual stimuliCode0
FogGuard: guarding YOLO against fog using perceptual lossCode0
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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