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 2926–2950 of 7282 papers

TitleStatusHype
O(d/T) Convergence Theory for Diffusion Probabilistic Models under Minimal Assumptions—0
Unsupervised Fingerphoto Presentation Attack Detection With Diffusion Models—0
Convergence of Diffusion Models Under the Manifold Hypothesis in High-Dimensions—0
Multi-hypotheses Conditioned Point Cloud Diffusion for 3D Human Reconstruction from Occluded ImagesCode0
Efficient Noise Mitigation for Enhancing Inference Accuracy in DNNs on Mixed-Signal Accelerators—0
Gradient-free Decoder Inversion in Latent Diffusion Models—0
Heterogeneous quantization regularizes spiking neural network activity—0
Joint Localization and Planning using Diffusion—0
DeBaRA: Denoising-Based 3D Room Arrangement Generation—0
Stable Video Portraits—0
AnyLogo: Symbiotic Subject-Driven Diffusion System with Gemini Status—0
Flexiffusion: Segment-wise Neural Architecture Search for Flexible Denoising Schedule—0
DiffSSC: Semantic LiDAR Scan Completion using Denoising Diffusion Probabilistic Models—0
Toward Efficient Deep Blind RAW Image Restoration—0
Enhancing Recommendation with Denoising Auxiliary Task—0
Generative Speech Foundation Model Pretraining for High-Quality Speech Extraction and Restoration—0
Denoising Graph Super-Resolution towards Improved Collider Event ReconstructionCode0
Multilingual Transfer and Domain Adaptation for Low-Resource Languages of Spain—0
ASD-Diffusion: Anomalous Sound Detection with Diffusion Models—0
ImPoster: Text and Frequency Guidance for Subject Driven Action Personalization using Diffusion ModelsCode0
Diffusion Models to Enhance the Resolution of Microscopy Images: A Tutorial—0
Mixture of Efficient Diffusion Experts Through Automatic Interval and Sub-Network Selection—0
EDGE-Rec: Efficient and Data-Guided Edge Diffusion For Recommender Systems Graphs—0
S^2AG-Vid: Enhancing Multi-Motion Alignment in Video Diffusion Models via Spatial and Syntactic Attention-Based Guidance—0
A Large Language Model and Denoising Diffusion Framework for Targeted Design of Microstructures with Commands in Natural Language—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