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 901–925 of 983 papers

TitleStatusHype
Learning to Kindle the StarlightCode0
A Two-stage Deep Network for High Dynamic Range Image ReconstructionCode0
A GAN-Enhanced Deep Learning Framework for Rooftop Detection from Historical Aerial ImageryCode0
Learning Hierarchical Dynamics with Spatial Adjacency for Image EnhancementCode0
Towards More Efficient EfficientDets and Low-Light Real-Time Marine Debris DetectionCode0
Towards Realistic Low-Light Image Enhancement via ISP Driven Data ModelingCode0
Removal of speckle noises from ultrasound images using five different deep learning networksCode0
Learning Converged Propagations with Deep Prior Ensemble for Image EnhancementCode0
UWFormer: Underwater Image Enhancement via a Semi-Supervised Multi-Scale TransformerCode0
Exploring Distortion Prior with Latent Diffusion Models for Remote Sensing Image CompressionCode0
Restoring Extremely Dark Images in Real TimeCode0
Joint Multi-Scale Tone Mapping and Denoising for HDR Image EnhancementCode0
VEDA: Uneven light image enhancement via a vision-based exploratory data analysis modelCode0
Make Lossy Compression Meaningful for Low-Light ImagesCode0
Distilling Style from Image Pairs for Global Forward and Inverse Tone MappingCode0
Rethinking the Atmospheric Scattering-driven Attention via Channel and Gamma Correction Priors for Low-Light Image EnhancementCode0
Intrinsic Image Transfer for Illumination ManipulationCode0
Improving Novel view synthesis of 360^ Scenes in Extremely Sparse Views by Jointly Training Hemisphere Sampled Synthetic ImagesCode0
Diffusion-Based Adaptation for Classification of Unknown Degraded ImagesCode0
Image Super-Resolution as a Defense Against Adversarial AttacksCode0
Image Enhancement for Remote Photoplethysmography in a Low-Light EnvironmentCode0
Image Enhancement Based on Histogram-Guided Multiple Transformation Function EstimationCode0
Attention Guided Low-light Image Enhancement with a Large Scale Low-light Simulation DatasetCode0
RIS-GAN: Explore Residual and Illumination with Generative Adversarial Networks for Shadow RemovalCode0
Aesthetic-Driven Image Enhancement by Adversarial LearningCode0
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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