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 726–750 of 983 papers

TitleStatusHype
NLHD: A Pixel-Level Non-Local Retinex Model for Low-Light Image Enhancement—0
Evaluating Deep Neural Networks for Image Document Enhancement—0
Deep Neural Network-based Enhancement for Image and Video Streaming Systems: A Survey and Future Directions—0
Enhance to Read Better: A Multi-Task Adversarial Network for Handwritten Document Image EnhancementCode0
High-Frequency aware Perceptual Image Enhancement—0
MIEHDR CNN: Main Image Enhancement based Ghost-Free High Dynamic Range Imaging using Dual-Lens Systems—0
Overparametrization of HyperNetworks at Fixed FLOP-Count Enables Fast Neural Image Enhancement—0
LAFFNet: A Lightweight Adaptive Feature Fusion Network for Underwater Image Enhancement—0
A Two-stage Deep Network for High Dynamic Range Image ReconstructionCode0
Unsupervised Hyperspectral Stimulated Raman Microscopy Image Enhancement: Denoising and Segmentation via One-Shot Deep LearningCode0
Advanced Image Enhancement Method for Distant Vessels and Structures in Capsule Endoscopy—0
Enhancing Underwater Image via Adaptive Color and Contrast Enhancement, and Denoising—0
Personalizing image enhancement for critical visual tasks: improved legibility of papyri using color processing and visual illusions—0
Evaluating COPY-BLEND Augmentation for Low Level Vision Tasks—0
Towards Ultrafast MRI via Extreme k-Space Undersampling and Superresolution—0
K-means segmentation based-on lab color space for embryo detection in incubated egg—0
Color-Coded Symbology and New Computer Vision Tool to Predict the Historical Color Pallets of the Renaissance Oil Artworks—0
Plug-and-Play gradient-based denoisers applied to CT image enhancementCode0
Segmentasi Citra Menggunakan Metode Watershed Transform Berdasarkan Image Enhancement Dalam Mendeteksi Embrio Telur—0
Histogram Specification by Assignment of Optimal Unique Values—0
Quarter Laplacian Filter for Edge Aware Image Processing—0
Bridge the Vision Gap from Field to Command: A Deep Learning Network Enhancing Illumination and Details—0
Image Enhancement using Fuzzy Intensity Measure and Adaptive Clipping Histogram Equalization—0
Shed Various Lights on a Low-Light Image: Multi-Level Enhancement Guided by Arbitrary References—0
Transformers in Vision: A Survey—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