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 501–525 of 983 papers

TitleStatusHype
Depth Image Enhancement Using Local Tangent Plane Approximations—0
Design of Novel Algorithm and Architecture for Gaussian Based Color Image Enhancement System for Real Time Applications—0
Detection of Parasitic Eggs from Microscopy Images and the emergence of a new dataset—0
Perceptual underwater image enhancement with deep learning and physical priors—0
DFR-TSD: A Deep Learning Based Framework for Robust Traffic Sign Detection Under Challenging Weather Conditions—0
DGNet: Dynamic Gradient-Guided Network for Water-Related Optics Image Enhancement—0
Multimodal Super-Resolution: Discovering hidden physics and its application to fusion plasmas—0
Dictionary learning based image enhancement for rarity detection—0
DiffLLE: Diffusion-guided Domain Calibration for Unsupervised Low-light Image Enhancement—0
Diff-Retinex: Rethinking Low-light Image Enhancement with A Generative Diffusion Model—0
DiffuseRAW: End-to-End Generative RAW Image Processing for Low-Light Images—0
A ground-based dataset and a diffusion model for on-orbit low-light image enhancement—0
DILIE: Deep Internal Learning for Image Enhancement—0
Directing Mamba to Complex Textures: An Efficient Texture-Aware State Space Model for Image Restoration—0
DI-Retinex: Digital-Imaging Retinex Theory for Low-Light Image Enhancement—0
Diving Deeper into Underwater Image Enhancement: A Survey—0
Division Gets Better: Learning Brightness-Aware and Detail-Sensitive Representations for Low-Light Image Enhancement—0
DN-ResNet: Efficient Deep Residual Network for Image Denoising—0
Does V-NIR based Image Enhancement Come with Better Features?—0
Domain Adaptive Adversarial Learning Based on Physics Model Feedback for Underwater Image Enhancement—0
Dream-IF: Dynamic Relative EnhAnceMent for Image Fusion—0
DREAM: On hallucinations in AI-generated content for nuclear medicine imaging—0
Dropout the High-rate Downsampling: A Novel Design Paradigm for UHD Image Restoration—0
Dual-Camera Joint Deblurring-Denoising—0
Dual Degradation-Inspired Deep Unfolding Network for Low-Light Image Enhancement—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