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 1826–1850 of 7282 papers

TitleStatusHype
Magic Mushroom: A Customizable Benchmark for Fine-grained Analysis of Retrieval Noise Erosion in RAG Systems—0
YOND: Practical Blind Raw Image Denoising Free from Camera-Specific Data Dependency—0
CHIME: Conditional Hallucination and Integrated Multi-scale Enhancement for Time Series Diffusion Model—0
Unrolling Nonconvex Graph Total Variation for Image Denoising—0
FreeScene: Mixed Graph Diffusion for 3D Scene Synthesis from Free Prompts—0
InfiniteAudio: Infinite-Length Audio Generation with Consistency—0
ADFormer: Aggregation Differential Transformer for Passenger Demand ForecastingCode0
Channel-adaptive Cross-modal Generative Semantic Communication for Point Cloud Transmission—0
Image Generation from Contextually-Contradictory Prompts—0
NTIRE 2025 the 2nd Restore Any Image Model (RAIM) in the Wild Challenge—0
ReconXF: Graph Reconstruction Attack via Public Feature Explanations on Privatized Node Features and Labels—0
Psi-Sampler: Initial Particle Sampling for SMC-Based Inference-Time Reward Alignment in Score Models—0
ProtInvTree: Deliberate Protein Inverse Folding with Reward-guided Tree Search—0
Towards Effective and Efficient Adversarial Defense with Diffusion Models for Robust Visual TrackingCode0
Latent Wavelet Diffusion: Enabling 4K Image Synthesis for Free—0
DeGLIF for Label Noise Robust Node Classification using GNNs—0
On Designing Diffusion Autoencoders for Efficient Generation and Representation LearningCode0
RealDrive: Retrieval-Augmented Driving with Diffusion Models—0
Aligning Protein Conformation Ensemble Generation with Physical Feedback—0
Diffusion-Based Symbolic Regression—0
STAR-Net: An Interpretable Model-Aided Network for Remote Sensing Image DenoisingCode0
TumorGen: Boundary-Aware Tumor-Mask Synthesis with Rectified Flow Matching—0
ComposeAnything: Composite Object Priors for Text-to-Image Generation—0
A Composite Predictive-Generative Approach to Monaural Universal Speech Enhancement—0
Interactive Video Generation via Domain Adaptation—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