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 826–850 of 7282 papers

TitleStatusHype
Federated Discrete Denoising Diffusion Model for Molecular Generation with OpenFL—0
Interaction Dataset of Autonomous Vehicles with Traffic Lights and Signs—0
VipDiff: Towards Coherent and Diverse Video Inpainting via Training-free Denoising Diffusion Models—0
Unified 3D MRI Representations via Sequence-Invariant Contrastive LearningCode0
Teacher Encoder-Student Decoder Denoising Guided Segmentation Network for Anomaly Detection—0
Efficient Bearing Sensor Data Compression via an Asymmetrical Autoencoder with a Lifting Wavelet Transform Layer—0
CNN-based TEM image denoising from first principles—0
DenoMAE: A Multimodal Autoencoder for Denoising Modulation SignalsCode0
Graph Defense Diffusion Model—0
Nested Annealed Training Scheme for Generative Adversarial Networks—0
Rethinking Pseudo-Label Guided Learning for Weakly Supervised Temporal Action Localization from the Perspective of Noise Correction—0
SCDM: Score-Based Channel Denoising Model for Digital Semantic Communications—0
DFingerNet: Noise-Adaptive Speech Enhancement for Hearing Aids—0
Inference-Time Alignment in Diffusion Models with Reward-Guided Generation: Tutorial and ReviewCode0
Inference-Time Scaling for Diffusion Models beyond Scaling Denoising Steps—0
Bias for Action: Video Implicit Neural Representations with Bias Modulation—0
Soft Knowledge Distillation with Multi-Dimensional Cross-Net Attention for Image Restoration Models Compression—0
Ouroboros-Diffusion: Exploring Consistent Content Generation in Tuning-free Long Video Diffusion—0
Generative diffusion model with inverse renormalization group flowsCode1
NeurOp-Diff:Continuous Remote Sensing Image Super-Resolution via Neural Operator DiffusionCode1
Watermarking in Diffusion Model: Gaussian Shading with Exact Diffusion Inversion via Coupled Transformations (EDICT)—0
Learning Joint Denoising, Demosaicing, and Compression from the Raw Natural Image Noise DatasetCode0
Decision Transformers for RIS-Assisted Systems with Diffusion Model-Based Channel Acquisition—0
Robust Low-Light Human Pose Estimation through Illumination-Texture Modulation—0
D^2-DPM: Dual Denoising for Quantized Diffusion Probabilistic ModelsCode1
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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