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 851–900 of 983 papers

TitleStatusHype
Low-light Image Enhancement Algorithm Based on Retinex and Generative Adversarial Network—0
Style Generator Inversion for Image Enhancement and AnimationCode0
Exemplar based underwater image enhancement augmented by Wavelet Corrected TransformsCode0
All-In-One Underwater Image Enhancement using Domain-Adversarial LearningCode0
DHSGAN: An End to End Dehazing Network for Fog and SmokeCode0
Contrast Enhancement of Medical X-Ray Image Using Morphological Operators with Optimal Structuring Element—0
Convolutional Neural Networks Considering Local and Global features for Image Enhancement—0
Context-Aware Automatic Occlusion RemovalCode0
Task-GAN for Improved GAN based Image Restoration—0
An approach to image denoising using manifold approximation without clean images—0
Learning a No-Reference Quality Assessment Model of Enhanced Images With Big Data—0
Fast Underwater Image Enhancement for Improved Visual PerceptionCode0
Stable Backward Diffusion Models that Minimise Convex Energies—0
Image Enhancement Network Trained by Using HDR images—0
Real-world Underwater Enhancement: Challenges, Benchmarks, and SolutionsCode0
An Underwater Image Enhancement Benchmark Dataset and BeyondCode0
Image Super-Resolution as a Defense Against Adversarial AttacksCode0
Fast Perceptual Image EnhancementCode0
Can Image Enhancement be Beneficial to Find Smoke Images in Laparoscopic Surgery?—0
Color Image Enhancement Method Based on Weighted Image Guided Filtering—0
Efficient Super Resolution Using Binarized Neural Network—0
Biometric Recognition System (Algorithm)—0
XNet: A convolutional neural network (CNN) implementation for medical X-Ray image segmentation suitable for small datasetsCode0
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
Lesion Focused Super-ResolutionCode1
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
Deep Photo Enhancer: Unpaired Learning for Image Enhancement From Photographs With GANsCode0
Classification-Driven Dynamic Image Enhancement—0
Enabling Pedestrian Safety using Computer Vision Techniques: A Case Study of the 2018 Uber Inc. Self-driving Car Crash—0
Show:102550
← PrevPage 18 of 20Next →
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