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 3851–3875 of 7282 papers

TitleStatusHype
GDTS: Goal-Guided Diffusion Model with Tree Sampling for Multi-Modal Pedestrian Trajectory Prediction—0
FreePIH: Training-Free Painterly Image Harmonization with Diffusion Model—0
DECap: Towards Generalized Explicit Caption Editing via Diffusion Mechanism—0
Highly Detailed and Temporal Consistent Video Stylization via Synchronized Multi-Frame Diffusion—0
Learning in Deep Factor Graphs with Gaussian Belief PropagationCode0
ToddlerDiffusion: Interactive Structured Image Generation with Cascaded Schrödinger Bridge—0
AdaDiff: Adaptive Step Selection for Fast Diffusion Models—0
D-SCo: Dual-Stream Conditional Diffusion for Monocular Hand-Held Object Reconstruction—0
ACT-Diffusion: Efficient Adversarial Consistency Training for One-step Diffusion ModelsCode0
EEG Connectivity Analysis Using Denoising Autoencoders for the Detection of Dyslexia—0
TDiffDe: A Truncated Diffusion Model for Remote Sensing Hyperspectral Image Denoising—0
Single Image Compressed Sensing MRI via a Self-Supervised Deep Denoising Approach—0
DiffusionMat: Alpha Matting as Sequential Refinement Learning—0
Recognition-Guided Diffusion Model for Scene Text Image Super-Resolution—0
FrePolad: Frequency-Rectified Point Latent Diffusion for Point Cloud Generation—0
An Image is Worth Multiple Words: Multi-attribute Inversion for Constrained Text-to-Image Synthesis—0
Gaussian Interpolation Flows—0
Mixing-Denoising Generalizable Occupancy Networks—0
GaussianDiffusion: 3D Gaussian Splatting for Denoising Diffusion Probabilistic Models with Structured Noise—0
DiffSCI: Zero-Shot Snapshot Compressive Imaging via Iterative Spectral Diffusion ModelCode0
SDDPM: Speckle Denoising Diffusion Probabilistic Models—0
FedDiff: Diffusion Model Driven Federated Learning for Multi-Modal and Multi-Clients—0
An Attention-Based Denoising Framework for Personality Detection in Social Media TextsCode0
DECDM: Document Enhancement using Cycle-Consistent Diffusion Models—0
An adaptive denoising recommendation algorithm for causal separation biasCode0
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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