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
Performance Evaluation of Infrared Image Enhancement Techniques—0
Photo-unrealistic Image Enhancement for Subject Placement in Outdoor Photography—0
Photovoltaic module segmentation and thermal analysis tool from thermal images—0
Semi-supervised Underwater Image Enhancement Using A Physics-Aware Triple-Stream Network—0
PIRM Challenge on Perceptual Image Enhancement on Smartphones: Report—0
Positron Emission Tomography (PET) image enhancement using a gradient vector orientation based nonlinear diffusion filter (GVOF) for accurate quantitation of radioactivity concentration—0
PQDynamicISP: Dynamically Controlled Image Signal Processor for Any Image Sensors Pursuing Perceptual Quality—0
Predicting Rapid Fire Growth (Flashover) Using Conditional Generative Adversarial Networks—0
Progressive Retinex: Mutually Reinforced Illumination-Noise Perception Network for Low Light Image Enhancement—0
Projection image-to-image translation in hybrid X-ray/MR imaging—0
Projection-to-Projection Translation for Hybrid X-ray and Magnetic Resonance Imaging—0
Properties on n-dimensional convolution for image deconvolution—0
Pyramid Texture Filtering—0
Quality Assessment for Comparing Image Enhancement Algorithms—0
Quality Enhancement of Radiographic X-ray Images by Interpretable Mapping—0
Quantitative and Qualitative Evaluation of NLM and Wavelet Methods in Image Enhancement—0
Quantum Annealing for Single Image Super-Resolution—0
Quarter Laplacian Filter for Edge Aware Image Processing—0
QUIET-SR: Quantum Image Enhancement Transformer for Single Image Super-Resolution—0
Rain Removal and Illumination Enhancement Done in One Go—0
Random Weights Networks Work as Loss Prior Constraint for Image Restoration—0
RB-Dust -- A Reference-based Dataset for Vision-based Dust Removal—0
Realistic Hair Synthesis with Generative Adversarial Networks—0
Real-time Image Enhancer via Learnable Spatial-aware 3D Lookup Tables—0
Real-world Instance-specific Image Goal Navigation: Bridging Domain Gaps via Contrastive Learning—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