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 701–725 of 983 papers

TitleStatusHype
Segmentasi Citra Menggunakan Metode Watershed Transform Berdasarkan Image Enhancement Dalam Mendeteksi Embrio Telur—0
Histogram Specification by Assignment of Optimal Unique Values—0
Twice Mixing: A Rank Learning based Quality Assessment Approach for Underwater Image EnhancementCode1
Underwater Image Enhancement via Learning Water Type Desensitized RepresentationsCode1
Bridge the Vision Gap from Field to Command: A Deep Learning Network Enhancing Illumination and Details—0
Quarter Laplacian Filter for Edge Aware Image Processing—0
Deep Convolutional Autoencoders for reconstructing magnetic resonance images of the healthy brainCode1
Image Enhancement using Fuzzy Intensity Measure and Adaptive Clipping Histogram Equalization—0
Shallow-UWnet : Compressed Model for Underwater Image EnhancementCode1
Shed Various Lights on a Low-Light Image: Multi-Level Enhancement Guided by Arbitrary References—0
Low Light Image Enhancement via Global and Local Context Modeling—0
Transformers in Vision: A Survey—0
A Switched View of Retinex: Deep Self-Regularized Low-Light Image Enhancement—0
STAR: A Structure-Aware Lightweight Transformer for Real-Time Image EnhancementCode1
Deep Symmetric Network for Underexposed Image Enhancement With Recurrent Attentional Learning—0
Representative Color Transform for Image EnhancementCode1
Perceptual Variousness Motion Deblurring With Light Global Context RefinementCode0
FGF-GAN: A Lightweight Generative Adversarial Network for Pansharpening via Fast Guided Filter—0
Unpaired Image Enhancement with Quality-Attention Generative Adversarial Network—0
Towards Unsupervised Deep Image Enhancement with Generative Adversarial NetworkCode1
Perception Consistency Ultrasound Image Super-resolution via Self-supervised CycleGANCode1
LEUGAN:Low-Light Image Enhancement by Unsupervised Generative Attentional Networks—0
UMLE: Unsupervised Multi-discriminator Network for Low Light Enhancement—0
Projected Distribution Loss for Image EnhancementCode0
SID-NISM: A Self-supervised Low-light Image Enhancement Framework—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