SOTAVerified

Denoising

Denoising is a task in image processing and computer vision that aims to remove or reduce noise from an image. Noise can be introduced into an image due to various reasons, such as camera sensor limitations, lighting conditions, and compression artifacts. The goal of denoising is to recover the original image, which is considered to be noise-free, from a noisy observation.

( Image credit: Beyond a Gaussian Denoiser )

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 2001–2025 of 7282 papers

TitleStatusHype
Improved Out-of-Scope Intent Classification with Dual Encoding and Threshold-based Re-ClassificationCode0
Recurrent Deep Kernel Learning of Dynamical Systems—0
All-In-One Medical Image Restoration via Task-Adaptive RoutingCode2
P-MSDiff: Parallel Multi-Scale Diffusion for Remote Sensing Image SegmentationCode0
LED: A Large-scale Real-world Paired Dataset for Event Camera DenoisingCode0
MotionFollower: Editing Video Motion via Lightweight Score-Guided DiffusionCode3
DP-IQA: Utilizing Diffusion Prior for Blind Image Quality Assessment in the WildCode1
Diffusion Policies creating a Trust Region for Offline Reinforcement LearningCode1
On the Condition Monitoring of Bolted Joints through Acoustic Emission and Deep Transfer Learning: Generalization, Ordinal Loss and Super-Convergence—0
RNAFlow: RNA Structure & Sequence Design via Inverse Folding-Based Flow MatchingCode2
Zero-to-Hero: Enhancing Zero-Shot Novel View Synthesis via Attention Map Filtering—0
Contrastive-Adversarial and Diffusion: Exploring pre-training and fine-tuning strategies for sulcal identification—0
Going beyond Compositions, DDPMs Can Produce Zero-Shot InterpolationsCode0
Improving global awareness of linkset predictions using Cross-Attentive Modulation tokens—0
PureGen: Universal Data Purification for Train-Time Poison Defense via Generative Model DynamicsCode1
MixDQ: Memory-Efficient Few-Step Text-to-Image Diffusion Models with Metric-Decoupled Mixed Precision Quantization—0
VITON-DiT: Learning In-the-Wild Video Try-On from Human Dance Videos via Diffusion Transformers—0
A Refined 3D Gaussian Representation for High-Quality Dynamic Scene Reconstruction—0
OV-DQUO: Open-Vocabulary DETR with Denoising Text Query Training and Open-World Unknown Objects SupervisionCode1
Diffusion Model Patching via Mixture-of-Prompts—0
Joint Channel, Data, and Radar Parameter Estimation for AFDM Systems in Doubly-Dispersive Channels—0
PatchScaler: An Efficient Patch-Independent Diffusion Model for Image Super-ResolutionCode1
Balancing User Preferences by Social Networks: A Condition-Guided Social Recommendation Model for Mitigating Popularity BiasCode0
Ensembling Diffusion Models via Adaptive Feature AggregationCode0
Glauber Generative Model: Discrete Diffusion Models via Binary Classification—0
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All datasetsDarmstadt Noise DatasetDIV2KDNDiris

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1SINDyPSNR81—Unverified
2Pixel-shuffling DownsamplingPSNR38.4—Unverified
3TWSCPSNR37.93—Unverified
4CBDNet(Syn)PSNR37.57—Unverified
5MCWNNMPSNR37.38—Unverified
6Han et alPSNR35.95—Unverified
7FFDNetPSNR34.4—Unverified
8TNRDPSNR33.65—Unverified
9CDnCNN-BPSNR32.43—Unverified
10NLRNPSNR30.8—Unverified
#ModelMetricClaimedVerifiedStatus
1DRUnet_Poisson_0.01Average PSNR (dB)33.92—Unverified
#ModelMetricClaimedVerifiedStatus
1DRANetAverage PSNR39.64—Unverified
#ModelMetricClaimedVerifiedStatus
1PCNN+RL+HMEAverage84.61—Unverified