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 1801–1825 of 7282 papers

TitleStatusHype
WD-DETR: Wavelet Denoising-Enhanced Real-Time Object Detection Transformer for Robot Perception with Event Cameras—0
Grids Often Outperform Implicit Neural RepresentationsCode0
Diffusion Models for Safety Validation of Autonomous Driving Systems—0
Enhancing Motion Dynamics of Image-to-Video Models via Adaptive Low-Pass Guidance—0
Plug-and-Play Linear Attention for Pre-trained Image and Video Restoration ModelsCode0
A Simple Analysis of Discretization Error in Diffusion Models—0
Surgeon Style Fingerprinting and Privacy Risk Quantification via Discrete Diffusion Models in a Vision-Language-Action FrameworkCode0
Diffusion models under low-noise regimeCode0
Diffusion Models-Aided Uplink Channel Estimation for RIS-Assisted Systems—0
Multi-Step Guided Diffusion for Image Restoration on Edge Devices: Toward Lightweight Perception in Embodied AI—0
Conditional Denoising Diffusion for ISAC Enhanced Channel Estimation in Cell-Free 6G—0
Denoising Programming Knowledge Tracing with a Code Graph-based Tuning Adaptor—0
Diffusion-Based Hierarchical Graph Neural Networks for Simulating Nonlinear Solid Mechanics—0
Latent Diffusion Model Based Denoising Receiver for 6G Semantic Communication: From Stochastic Differential Theory to Application—0
FPSAttention: Training-Aware FP8 and Sparsity Co-Design for Fast Video Diffusion—0
S2GO: Streaming Sparse Gaussian Occupancy Prediction—0
MARBLE: Material Recomposition and Blending in CLIP-Space—0
Pseudo-Siamese Blind-Spot Transformers for Self-Supervised Real-World DenoisingCode0
How to Unlock Time Series Editing? Diffusion-Driven Approach with Multi-Grained Control—0
Learning normalized image densities via dual score matchingCode0
DiffCAP: Diffusion-based Cumulative Adversarial Purification for Vision Language Models—0
Magic Mushroom: A Customizable Benchmark for Fine-grained Analysis of Retrieval Noise Erosion in RAG Systems—0
SVD-Based Graph Fractional Fourier Transform on Directed Graphs and Its Application—0
A Poisson-Guided Decomposition Network for Extreme Low-Light Image Enhancement—0
Diffusion Transformer-based Universal Dose Denoising for Pencil Beam Scanning Proton Therapy—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