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 876–900 of 983 papers

TitleStatusHype
IEGAN: Multi-purpose Perceptual Quality Image Enhancement Using Generative Adversarial Network—0
A Retinex-based Image Enhancement Scheme with Noise Aware Shadow-up Function—0
Underwater Single Image Color Restoration Using Haze-Lines and a New Quantitative DatasetCode0
DN-ResNet: Efficient Deep Residual Network for Image Denoising—0
Learning Converged Propagations with Deep Prior Ensemble for Image EnhancementCode0
SLIC Based Digital Image Enlargement—0
PIRM Challenge on Perceptual Image Enhancement on Smartphones: Report—0
Gradient-Based Low-Light Image Enhancement—0
Image contrast enhancement using fuzzy logic—0
Fast and Efficient Image Quality Enhancement via Desubpixel Convolutional Neural NetworksCode0
Reconstruction-based Pairwise Depth Dataset for Depth Image Enhancement Using CNN—0
Fractional Multiscale Fusion-based De-hazing—0
Deep Retinex Decomposition for Low-Light EnhancementCode0
Alpha-rooting color image enhancement method by two-side 2-D quaternion discrete Fourier transform followed by spatial transformation—0
A Novel Color Image Enhancement Method by the Transformation of Color Images to 2-D Grayscale Images—0
ISIC 2018-A Method for Lesion Segmentation—0
CT Image Enhancement Using Stacked Generative Adversarial Networks and Transfer Learning for Lesion Segmentation Improvement—0
Computed Tomography Image Enhancement using 3D Convolutional Neural Network—0
Photo-unrealistic Image Enhancement for Subject Placement in Outdoor Photography—0
Deep Underwater Image EnhancementCode0
Mammographic Image Enhancement using Digital Image Processing Technique—0
Single Image Reflection Separation with Perceptual LossesCode0
Stochastic Capsule Endoscopy Image EnhancementCode0
Classification-Driven Dynamic Image Enhancement—0
Deep Photo Enhancer: Unpaired Learning for Image Enhancement From Photographs With GANsCode0
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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