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 551–600 of 983 papers

TitleStatusHype
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
1st Place Solutions for UG2+ Challenge 2021 -- (Semi-)supervised Face detection in the low light condition—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
HipyrNet: Hypernet-Guided Feature Pyramid network for mixed-exposure correction—0
Histogram Specification by Assignment of Optimal Unique Values—0
HPGN: Hybrid Priors-Guided Network for Compressed Low-Light Image Enhancement—0
HQG-Net: Unpaired Medical Image Enhancement with High-Quality Guidance—0
HUE Dataset: High-Resolution Event and Frame Sequences for Low-Light Vision—0
Hybrid Transformer and CNN Attention Network for Stereo Image Super-resolution—0
HypervolGAN: An efficient approach for GAN with multi-objective training function—0
IA2U: A Transfer Plugin with Multi-Prior for In-Air Model to Underwater—0
IDA-UIE: An Iterative Framework for Deep Network-based Degradation Aware Underwater Image Enhancement—0
Identification of chicken egg fertility using SVM classifier based on first-order statistical feature extraction—0
Identity Encoder for Personalized Diffusion—0
IEGAN: Multi-purpose Perceptual Quality Image Enhancement Using Generative Adversarial Network—0
ILACS-LGOT: A Multi-Layer Contrast Enhancement Approach for Palm-Vein Images—0
Image-Conditional Diffusion Transformer for Underwater Image Enhancement—0
Image contrast enhancement using fuzzy logic—0
Image Edge Restoring Filter—0
Image Enhancement and Noise Reduction Using Modified Delay-Multiply-and-Sum Beamformer: Application to Medical Photoacoustic Imaging—0
Image Enhancement and Object Recognition for Night Vision Surveillance—0
Image Enhancement by Recurrently-trained Super-resolution Network—0
Image enhancement in acoustic-resolution photoacoustic microscopy enabled by a novel directional algorithm—0
Image enhancement in intensity projected multichannel MRI using spatially adaptive directional anisotropic diffusion—0
Image Enhancement Network Trained by Using HDR images—0
Image Enhancement Using a Generalization of Homographic Function—0
Image enhancement using fusion by wavelet transform and laplacian pyramid—0
Image Enhancement using Fuzzy Intensity Measure and Adaptive Clipping Histogram Equalization—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