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 251–275 of 983 papers

TitleStatusHype
Comparative Analysis of Image Enhancement Techniques for Brain Tumor Segmentation: Contrast, Histogram, and Hybrid Approaches—0
CodeEnhance: A Codebook-Driven Approach for Low-Light Image Enhancement—0
PHISWID: Physics-Inspired Underwater Image Dataset Synthesized from RGB-D ImagesCode0
DI-Retinex: Digital-Imaging Retinex Theory for Low-Light Image Enhancement—0
Specularity Factorization for Low-Light Enhancement—0
RAVE: Residual Vector Embedding for CLIP-Guided Backlit Image EnhancementCode1
CLIPtone: Unsupervised Learning for Text-based Image Tone Adjustment—0
Towards Robust Event-guided Low-Light Image Enhancement: A Large-Scale Real-World Event-Image Dataset and Novel Approach—0
A Real-Time Framework for Domain-Adaptive Underwater Object Detection with Image Enhancement—0
Burst Super-Resolution with Diffusion Models for Improving Perceptual QualityCode1
Taming Lookup Tables for Efficient Image RetouchingCode1
Test-time Adaptation Meets Image Enhancement: Improving Accuracy via Uncertainty-aware Logit Switching—0
Residual Dense Swin Transformer for Continuous Depth-Independent Ultrasound ImagingCode1
AdaIR: Adaptive All-in-One Image Restoration via Frequency Mining and ModulationCode3
X-CAUNET: Cross-Color Channel Attention with Underwater Image-Enhancing TransformerCode0
End-To-End Underwater Video Enhancement: Dataset and ModelCode1
PQDynamicISP: Dynamically Controlled Image Signal Processor for Any Image Sensors Pursuing Perceptual Quality—0
7T MRI Synthesization from 3T AcquisitionsCode1
FogGuard: guarding YOLO against fog using perceptual lossCode0
NightHaze: Nighttime Image Dehazing via Self-Prior Learning—0
Multiple Latent Space Mapping for Compressed Dark Image Enhancement—0
When No-Reference Image Quality Models Meet MAP Estimation in Diffusion Latents—0
Enhanced Low-Dose CT Image Reconstruction by Domain and Task Shifting Gaussian Denoisers—0
Speckle Noise Reduction in Ultrasound Images using Denoising Auto-encoder with Skip Connection—0
Zero-LED: Zero-Reference Lighting Estimation Diffusion Model 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