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 451–500 of 983 papers

TitleStatusHype
Unsharp Masking Layer: Injecting Prior Knowledge in Convolutional Networks for Image ClassificationCode0
Deep Underwater Image EnhancementCode0
Unsupervised Hyperspectral Stimulated Raman Microscopy Image Enhancement: Denoising and Segmentation via One-Shot Deep LearningCode0
UDBE: Unsupervised Diffusion-based Brightness Enhancement in Underwater ImagesCode0
Linear Array Network for Low-light Image EnhancementCode0
Task-GAN for Improved GAN based Image Restoration—0
TDiR: Transformer based Diffusion for Image Restoration Tasks—0
Temperature calibration of surface emissivities with an improved thermal image enhancement network—0
Test-time Adaptation Meets Image Enhancement: Improving Accuracy via Uncertainty-aware Logit Switching—0
Texture Matching GAN for CT Image Enhancement—0
The Affine Transforms for Image Enhancement in the Context of Logarithmic Models—0
The Loop Game: Quality Assessment and Optimization for Low-Light Image Enhancement—0
Topologically-Guided Color Image Enhancement—0
Towards A Robust Group-level Emotion Recognition via Uncertainty-Aware Learning—0
Towards Authentic Face Restoration with Iterative Diffusion Models and Beyond—0
Efficient Diffusion as Low Light Enhancer—0
Local Low-light Image Enhancement via Region-Aware Normalization—0
Towards Robust Event-guided Low-Light Image Enhancement: A Large-Scale Real-World Event-Image Dataset and Novel Approach—0
Towards Robust Low Light Image Enhancement—0
Towards Ultrafast MRI via Extreme k-Space Undersampling and Superresolution—0
Towards ultra-low-cost smartphone microscopy—0
TrainFors: A Large Benchmark Training Dataset for Image Manipulation Detection and Localization—0
Training Your Image Restoration Network Better with Random Weight Network as Optimization Function—0
Training Your Image Restoration Network Better with Random Weight Network as Optimization Function—0
Transformers in Vision: A Survey—0
Transmission and Color-guided Network for Underwater Image Enhancement—0
TSN-CA: A Two-Stage Network with Channel Attention for Low-Light Image Enhancement—0
Tunable Image Quality Control of 3-D Ultrasound using Switchable CycleGAN—0
Two Decades of Colorization and Decolorization for Images and Videos—0
Two-stage quality adaptive fingerprint image enhancement using Fuzzy c-means clustering based fingerprint quality analysis—0
UICE-MIRNet guided image enhancement for underwater object detection—0
UIF: An Objective Quality Assessment for Underwater Image Enhancement—0
UIT-Saviors at MEDVQA-GI 2023: Improving Multimodal Learning with Image Enhancement for Gastrointestinal Visual Question Answering—0
UKDM: Underwater keypoint detection and matching using underwater image enhancement techniques—0
UMLE: Unsupervised Multi-discriminator Network for Low Light Enhancement—0
Uncertainty-aware GAN with Adaptive Loss for Robust MRI Image Enhancement—0
Uncertainty Quantification in Deep Learning for Safer Neuroimage Enhancement—0
Underwater Diffusion Attention Network with Contrastive Language-Image Joint Learning for Underwater Image Enhancement—0
Underwater enhancement based on a self-learning strategy and attention mechanism for high-intensity regions—0
Underwater Image Enhancement Based on Structure-Texture Reconstruction—0
Underwater Image Enhancement Using Convolutional Neural Network—0
Underwater Image Enhancement using Generative Adversarial Networks: A Survey—0
Underwater Image Enhancement Using Pre-trained Transformer—0
Underwater Image Enhancement via Dehazing and Color Restoration—0
Underwater image enhancement with Image Colorfulness Measure—0
Unified-EGformer: Exposure Guided Lightweight Transformer for Mixed-Exposure Image Enhancement—0
Unified Representation Learning for Efficient Medical Image Analysis—0
Unifying Image Processing as Visual Prompting Question Answering—0
UnitModule: A Lightweight Joint Image Enhancement Module for Underwater Object Detection—0
Unlocking Masked Autoencoders as Loss Function for Image and Video Restoration—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
3AdaIntPSNR on proRGB25.49—Unverified
4RSFNet-mapPSNR 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