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 351–375 of 983 papers

TitleStatusHype
DEANet: Decomposition Enhancement and Adjustment Network for Low-Light Image Enhancement—0
Artwork painting identification method for panorama based on adaptive rectilinear projection and optimized ASIFT—0
Image Edge Restoring Filter—0
Image Enhancement by Recurrently-trained Super-resolution Network—0
Day2Dark: Pseudo-Supervised Activity Recognition beyond Silent Daylight—0
Artificial Intelligence-Based Image Enhancement in PET Imaging: Noise Reduction and Resolution Enhancement—0
DarkVision: A Benchmark for Low-light Image/Video Perception—0
ARIN: Adaptive Resampling and Instance Normalization for Robust Blind Inpainting of Dunhuang Cave Paintings—0
A Generative Adversarial Approach with Residual Learning for Dust and Scratches Artifacts Removal—0
DA-DRN: Degradation-Aware Deep Retinex Network for Low-Light Image Enhancement—0
A Survey of Fish Tracking Techniques Based on Computer Vision—0
Cycle-Interactive Generative Adversarial Network for Robust Unsupervised Low-Light Enhancement—0
Curved Gabor Filters for Fingerprint Image Enhancement—0
A review of advancements in low-light image enhancement using deep learning—0
A comprehensive benchmark analysis for sand dust image reconstruction—0
CURVE: CLIP-Utilized Reinforcement Learning for Visual Image Enhancement via Simple Image Processing—0
A Retinex-based Image Enhancement Scheme with Noise Aware Shadow-up Function—0
A Retinex based GAN Pipeline to Utilize Paired and Unpaired Datasets for Enhancing Low Light Images—0
CuDi: Curve Distillation for Efficient and Controllable Exposure Adjustment—0
A comparative study of paired versus unpaired deep learning methods for physically enhancing digital rock image resolution—0
CT Image Enhancement Using Stacked Generative Adversarial Networks and Transfer Learning for Lesion Segmentation Improvement—0
Cross-Domain Underwater Image Enhancement Guided by No-Reference Image Quality Assessment: A Transfer Learning Approach—0
A Retinal Image Enhancement Technique for Blood Vessel Segmentation Algorithm—0
CPDM: Content-Preserving Diffusion Model for Underwater Image Enhancement—0
A Real-Time Framework for Domain-Adaptive Underwater Object Detection with 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