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 776–800 of 983 papers

TitleStatusHype
Noise-Aware Texture-Preserving Low-Light Enhancement—0
Retaining Image Feature Matching Performance Under Low Light Conditions—0
Perceptual underwater image enhancement with deep learning and physical priors—0
Guided Collaborative Training for Pixel-wise Semi-Supervised Learning—0
Deep Photo Cropper and Enhancer—0
URIE: Universal Image Enhancement for Visual Recognition in the Wild—0
Global and Local Enhancement Networks for Paired and Unpaired Image EnhancementCode0
Exploring Image Enhancement for Salient Object Detection in Low Light Images—0
Decompose X-ray Images for Bone and Soft Tissue—0
Deep Bilateral Retinex for Low-Light Image Enhancement—0
A Benchmark dataset for both underwater image enhancement and underwater object detection—0
A Retinex based GAN Pipeline to Utilize Paired and Unpaired Datasets for Enhancing Low Light Images—0
HypervolGAN: An efficient approach for GAN with multi-objective training function—0
Feedback Graph Attention Convolutional Network for Medical Image Enhancement—0
Unified Representation Learning for Efficient Medical Image Analysis—0
Image Enhancement and Object Recognition for Night Vision Surveillance—0
Single Image Deraining via Scale-space Invariant Attention Neural Network—0
DFR-TSD: A Deep Learning Based Framework for Robust Traffic Sign Detection Under Challenging Weather Conditions—0
Low-light Image Enhancement Using the Cell Vibration ModelCode0
Learning to Restore Low-Light Images via Decomposition-and-Enhancement—0
From Fidelity to Perceptual Quality: A Semi-Supervised Approach for Low-Light Image Enhancement—0
Fast Enhancement for Non-Uniform Illumination Images using Light-weight CNNs—0
Positron Emission Tomography (PET) image enhancement using a gradient vector orientation based nonlinear diffusion filter (GVOF) for accurate quantitation of radioactivity concentration—0
Attention-based network for low-light image enhancement—0
Color-wise Attention Network for Low-light 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