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 651–675 of 983 papers

TitleStatusHype
Lightweight Image Enhancement Network for Mobile Devices Using Self-Feature Extraction and Dense Modulation—0
Two Decades of Colorization and Decolorization for Images and Videos—0
Image Restoration in Non-Linear Filtering Domain using MDB approach—0
UID2021: An Underwater Image Dataset for Evaluation of No-reference Quality Assessment MetricsCode0
Learning Enriched Features for Fast Image Restoration and Enhancement—0
Underwater Image Enhancement Using Pre-trained Transformer—0
Lightweight HDR Camera ISP for Robust Perception in Dynamic Illumination Conditions via Fourier Adversarial Networks—0
Extremely Low-light Image Enhancement with Scene Text RestorationCode0
MyStyle: A Personalized Generative Prior—0
Stable Optimization for Large Vision Model Based Deep Image Prior in Cone-Beam CT Reconstruction—0
Parametric Scaling of Preprocessing assisted U-net Architecture for Improvised Retinal Vessel Segmentation—0
Medium Transmission Map Matters for Learning to Restore Real-World Underwater ImagesCode0
Towards More Efficient EfficientDets and Low-Light Real-Time Marine Debris DetectionCode0
LFW-Beautified: A Dataset of Face Images with Beautification and Augmented Reality FiltersCode0
Detection of Parasitic Eggs from Microscopy Images and the emergence of a new dataset—0
The Loop Game: Quality Assessment and Optimization for Low-Light Image Enhancement—0
A Wavelet-based Dual-stream Network for Underwater Image Enhancement—0
Learning to Adapt to Light—0
Low-light Image Enhancement by Retinex Based Algorithm Unrolling and Adjustment—0
Accurate super-resolution low-field brain MRI—0
Performance Evaluation of Infrared Image Enhancement Techniques—0
A comprehensive benchmark analysis for sand dust image reconstruction—0
S2MS: Self-Supervised Learning Driven Multi-Spectral CT Image Enhancement—0
Linear Array Network for Low-light Image EnhancementCode0
Enhancing Low-Light Images in Real World via Cross-Image Disentanglement—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