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 401–425 of 983 papers

TitleStatusHype
Application of Compromising Evolution in Multi-objective Image Error Concealment—0
High-Frequency aware Perceptual Image Enhancement—0
FreeEnhance: Tuning-Free Image Enhancement via Content-Consistent Noising-and-Denoising Process—0
Fractional Multiscale Fusion-based De-hazing—0
Fractional Calculus In Image Processing: A Review—0
A framework for river connectivity classification using temporal image processing and attention based neural networks—0
A Comparative Study of Histogram Equalization Based Image Enhancement Techniques for Brightness Preservation and Contrast Enhancement—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
ITRE: Low-light Image Enhancement Based on Illumination Transmission Ratio Estimation—0
Joint Perceptual Learning for Enhancement and Object Detection in Underwater Scenarios—0
Forensic Video Analytic Software—0
A PDE-based log-agnostic illumination correction algorithm—0
AOSR-Net: All-in-One Sandstorm Removal Network—0
Super-Resolution Generative Adversarial Networks based Video Enhancement—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
Flexible Example-based Image Enhancement with Task Adaptive Global Feature Self-Guided Network—0
Computed Tomography Image Enhancement using 3D Convolutional Neural Network—0
Iterative Prompt Learning for Unsupervised Backlit 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