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 2051–2075 of 7282 papers

TitleStatusHype
Bringing together invertible UNets with invertible attention modules for memory-efficient diffusion models—0
IlluSign: Illustrating Sign Language Videos by Leveraging the Attention Mechanism—0
Efficient Medical Image Restoration via Reliability Guided Learning in Frequency Domain—0
AgentPolyp: Accurate Polyp Segmentation via Image Enhancement Agent—0
Cross-Frequency Implicit Neural Representation with Self-Evolving Parameters—0
Taming Consistency Distillation for Accelerated Human Image Animation—0
Efficient and Robust Remote Sensing Image Denoising Using Randomized Approximation of Geodesics' Gramian on the Manifold Underlying the Patch Space—0
ADT: Tuning Diffusion Models with Adversarial Supervision—0
Predicting Wave Dynamics using Deep Learning with Multistep Integration Inspired Attention and Physics-Based Loss Decomposition—0
InterAnimate: Taming Region-aware Diffusion Model for Realistic Human Interaction Animation—0
Score Matching Diffusion Based Feedback Control and Planning of Nonlinear Systems—0
Ising Models with Hidden Markov Structure: Applications to Probabilistic Inference in Machine Learning—0
Separate to Collaborate: Dual-Stream Diffusion Model for Coordinated Piano Hand Motion Synthesis—0
VibrantLeaves: A principled parametric image generator for training deep restoration modelsCode0
Decoupled Diffusion Sparks Adaptive Scene Generation—0
H3AE: High Compression, High Speed, and High Quality AutoEncoder for Video Diffusion Models—0
DiffMOD: Progressive Diffusion Point Denoising for Moving Object Detection in Remote Sensing—0
Mitigating Long-tail Distribution in Oracle Bone Inscriptions: Dataset, Model, and Benchmark—0
AB-Cache: Training-Free Acceleration of Diffusion Models via Adams-Bashforth Cached Feature Reuse—0
Imaging Transformer for MRI Denoising: a Scalable Model Architecture that enables SNR << 1 Imaging—0
Computationally iterative methods for salt-and-pepper denoising—0
seg2med: a bridge from artificial anatomy to multimodal medical images—0
UniFlowRestore: A General Video Restoration Framework via Flow Matching and Prompt Guidance—0
Training-free Guidance in Text-to-Video Generation via Multimodal Planning and Structured Noise Initialization—0
V2V3D: View-to-View Denoised 3D Reconstruction for Light-Field Microscopy—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