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

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Showing 276–300 of 7282 papers

TitleStatusHype
Conformal Bounds on Full-Reference Image Quality for Imaging Inverse ProblemsCode0
Diffusion Recommender Models and the Illusion of Progress: A Concerning Study of Reproducibility and a Conceptual Mismatch—0
Generating Full-field Evolution of Physical Dynamics from Irregular Sparse Observations—0
Denoising and Alignment: Rethinking Domain Generalization for Multimodal Face Anti-Spoofing—0
Marigold: Affordable Adaptation of Diffusion-Based Image Generators for Image AnalysisCode7
TransDiffuser: End-to-end Trajectory Generation with Decorrelated Multi-modal Representation for Autonomous Driving—0
Highly Undersampled MRI Reconstruction via a Single Posterior Sampling of Diffusion ModelsCode0
Learning Cocoercive Conservative Denoisers via Helmholtz Decomposition for Poisson Inverse Problems—0
Total Variation-Based Image Decomposition and Denoising for Microscopy ImagesCode1
Towards Autonomous UAV Visual Object Search in City Space: Benchmark and Agentic Methodology—0
Behind the Noise: Conformal Quantile Regression Reveals Emergent Representations—0
ConDiSim: Conditional Diffusion Models for Simulation Based Inference—0
EventDiff: A Unified and Efficient Diffusion Model Framework for Event-based Video Frame Interpolation—0
SparseMeXT Unlocking the Potential of Sparse Representations for HD Map Construction—0
Channel Fingerprint Construction for Massive MIMO: A Deep Conditional Generative Approach—0
You Only Look One Step: Accelerating Backpropagation in Diffusion Sampling with Gradient ShortcutsCode0
DanceGRPO: Unleashing GRPO on Visual GenerationCode5
A Self-Supervised Method for Attenuating Seismic Random and Tracewise Coherent Noise under the Non-Pixelwise Independence AssumptionCode0
Topology Guidance: Controlling the Outputs of Generative Models via Vector Field Topology—0
Technical Report for ICRA 2025 GOOSE 2D Semantic Segmentation Challenge: Leveraging Color Shift Correction, RoPE-Swin Backbone, and Quantile-based Label Denoising Strategy for Robust Outdoor Scene Understanding—0
Burger: Robust Graph Denoising-augmentation Fusion and Multi-semantic Modeling in Social Recommendation—0
ProFashion: Prototype-guided Fashion Video Generation with Multiple Reference Images—0
Computationally Efficient Diffusion Models in Medical Imaging: A Comprehensive Review—0
Insertion Language Models: Sequence Generation with Arbitrary-Position Insertions—0
Automated Learning of Semantic Embedding Representations for Diffusion Models—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