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 701–750 of 983 papers

TitleStatusHype
Early Detection of Retinopathy of Prematurity stage using Deep Learning approach—0
Edge-guided Low-light Image Enhancement with Inertial Bregman Alternating Linearized Minimization—0
Efficient Super Resolution Using Binarized Neural Network—0
Embedding Fourier for Ultra-High-Definition Low-Light Image Enhancement—0
Enabling Pedestrian Safety using Computer Vision Techniques: A Case Study of the 2018 Uber Inc. Self-driving Car Crash—0
Encoding in the Dark Grand Challenge: An Overview—0
Enhanced Confocal Laser Scanning Microscopy with Adaptive Physics Informed Deep Autoencoders—0
Enhanced total variation minimization for stable image reconstruction—0
Enhancement by Your Aesthetic: An Intelligible Unsupervised Personalized Enhancer for Low-Light Images—0
Enhancement of Underwater Images with Statistical Model of Background Light and Optimization of Transmission Map—0
Enhancing Low-Light Images in Real World via Cross-Image Disentanglement—0
Enhancing Low-Light Images Using Infrared-Encoded Images—0
Enhancing Supermarket Robot Interaction: A Multi-Level LLM Conversational Interface for Handling Diverse Customer Intents—0
Enhancing the Accuracy of Biometric Feature Extraction Fusion Using Gabor Filter and Mahalanobis Distance Algorithm—0
Enhancing Underwater Image via Adaptive Color and Contrast Enhancement, and Denoising—0
ERD: Exponential Retinex decomposition based on weak space and hybrid nonconvex regularization and its denoising application—0
ERUP-YOLO: Enhancing Object Detection Robustness for Adverse Weather Condition by Unified Image-Adaptive Processing—0
Evaluating COPY-BLEND Augmentation for Low Level Vision Tasks—0
Evaluating Deep Neural Networks for Image Document Enhancement—0
Exploring Image Enhancement for Salient Object Detection in Low Light Images—0
Exploring Low-light Object Detection Techniques—0
Exposure Normalization and Compensation for Multiple-Exposure Correction—0
Face Image Lighting Enhancement Using a 3D Model—0
Face Image Quality Enhancement Study for Face Recognition—0
Fast Dust Sand Image Enhancement Based on Color Correction and New Membership Function—0
Fast Enhancement for Non-Uniform Illumination Images using Light-weight CNNs—0
Feature Attention Network (FA-Net): A Deep-Learning Based Approach for Underwater Single Image Enhancement—0
Feature Enhancer Segmentation Network (FES-Net) for Vessel Segmentation—0
Feedback Graph Attention Convolutional Network for Medical Image Enhancement—0
FGF-GAN: A Lightweight Generative Adversarial Network for Pansharpening via Fast Guided Filter—0
Fingerprint Extraction Using Smartphone Camera—0
Flexible Example-based Image Enhancement with Task Adaptive Global Feature Self-Guided Network—0
Forensic Video Analytic Software—0
Fractional Calculus In Image Processing: A Review—0
Fractional Multiscale Fusion-based De-hazing—0
FreeEnhance: Tuning-Free Image Enhancement via Content-Consistent Noising-and-Denoising Process—0
From Fidelity to Perceptual Quality: A Semi-Supervised Approach for Low-Light Image Enhancement—0
FusionNet: Multi-model Linear Fusion Framework for Low-light Image Enhancement—0
Generalized Task-Driven Medical Image Quality Enhancement with Gradient Promotion—0
GIQE: Generic Image Quality Enhancement via Nth Order Iterative Degradation—0
GM-MoE: Low-Light Enhancement with Gated-Mechanism Mixture-of-Experts—0
Going the Extra Mile in Face Image Quality Assessment: A Novel Database and Model—0
Gradient-Based Low-Light Image Enhancement—0
Gray Level Image Enhancement Using Polygonal Functions—0
Gray level image enhancement using the Bernstein polynomials—0
Guided Collaborative Training for Pixel-wise Semi-Supervised Learning—0
Guided Colorization Using Mono-Color Image Pairs—0
High Accurate Unhealthy Leaf Detection—0
High Density Noise Removal by Cascading Algorithms—0
High-Frequency aware Perceptual 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
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