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 376–400 of 983 papers

TitleStatusHype
Analyzing Noise Models and Advanced Filtering Algorithms for Image EnhancementCode0
Deep Learning, Machine Learning -- Digital Signal and Image Processing: From Theory to Application—0
HUE Dataset: High-Resolution Event and Frame Sequences for Low-Light Vision—0
An Improved Chicken Swarm Optimization Algorithm for Handwritten Document Image EnhancementCode0
Efficient Diffusion as Low Light Enhancer—0
LIME-Eval: Rethinking Low-light Image Enhancement Evaluation via Object DetectionCode0
Image Enhancement Based on Histogram-Guided Multiple Transformation Function EstimationCode0
Generalizability analysis of deep learning predictions of human brain responses to augmented and semantically novel visual stimuliCode0
GMT: Enhancing Generalizable Neural Rendering via Geometry-Driven Multi-Reference Texture TransferCode0
PDCFNet: Enhancing Underwater Images through Pixel Difference ConvolutionCode0
Unsupervised Low-light Image Enhancement with Lookup Tables and Diffusion Priors—0
SinoSynth: A Physics-based Domain Randomization Approach for Generalizable CBCT Image EnhancementCode0
Morphological-consistent Diffusion Network for Ultrasound Coronal Image Enhancement—0
UICE-MIRNet guided image enhancement for underwater object detection—0
Low-Light Enhancement Effect on Classification and Detection: An Empirical Study—0
Quantitative and Qualitative Evaluation of NLM and Wavelet Methods in Image Enhancement—0
Underwater Image Enhancement via Dehazing and Color Restoration—0
Context-Aware Optimal Transport Learning for Retinal Fundus Image EnhancementCode0
FreeEnhance: Tuning-Free Image Enhancement via Content-Consistent Noising-and-Denoising Process—0
Unrevealed Threats: A Comprehensive Study of the Adversarial Robustness of Underwater Image Enhancement Models—0
Modeling Image Tone Dichotomy with the Power Function—0
Rethinking the Atmospheric Scattering-driven Attention via Channel and Gamma Correction Priors for Low-Light Image EnhancementCode0
RCNet: Deep Recurrent Collaborative Network for Multi-View Low-Light Image EnhancementCode0
SINET: Sparsity-driven Interpretable Neural Network for Underwater Image EnhancementCode0
Enhancing Underwater Imaging with 4-D Light Fields: Dataset and MethodCode0
Show:102550
← PrevPage 16 of 40Next →
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