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

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Showing 751–775 of 7282 papers

TitleStatusHype
In-context denoising with one-layer transformers: connections between attention and associative memory retrieval—0
Robust Graph Learning Against Adversarial Evasion Attacks via Prior-Free Diffusion-Based Structure PurificationCode0
How vulnerable is my policy? Adversarial attacks on modern behavior cloning policies—0
A Mixture-Based Framework for Guiding Diffusion ModelsCode1
Taking a Big Step: Large Learning Rates in Denoising Score Matching Prevent Memorization—0
TruePose: Human-Parsing-guided Attention Diffusion for Full-ID Preserving Pose Transfer—0
Data denoising with self consistency, variance maximization, and the Kantorovich dominance—0
SLCGC: A lightweight Self-supervised Low-pass Contrastive Graph Clustering Network for Hyperspectral Images—0
On-device Sora: Enabling Training-Free Diffusion-based Text-to-Video Generation for Mobile DevicesCode2
Controllable Satellite-to-Street-View Synthesis with Precise Pose Alignment and Zero-Shot Environmental Control—0
InterLCM: Low-Quality Images as Intermediate States of Latent Consistency Models for Effective Blind Face Restoration—0
Towards Consistent and Controllable Image Synthesis for Face Editing—0
T-SCEND: Test-time Scalable MCTS-enhanced Diffusion ModelCode1
Diff9D: Diffusion-Based Domain-Generalized Category-Level 9-DoF Object Pose EstimationCode2
Exploring the latent space of diffusion models directly through singular value decomposition—0
CoDe: Blockwise Control for Denoising Diffusion ModelsCode0
Accelerating Linear Recurrent Neural Networks for the Edge with Unstructured Sparsity—0
Sparse Measurement Medical CT Reconstruction using Multi-Fused Block Matching Denoising Priors—0
A generative foundation model for an all-in-one seismic processing framework—0
Compressed Image Generation with Denoising Diffusion Codebook ModelsCode2
CAT Pruning: Cluster-Aware Token Pruning For Text-to-Image Diffusion ModelsCode0
Personalized Denoising Implicit Feedback for Robust Recommender SystemCode0
Semantic Communication based on Generative AI: A New Approach to Image Compression and Edge Optimization—0
PM-MOE: Mixture of Experts on Private Model Parameters for Personalized Federated LearningCode1
Patch Triplet Similarity Purification for Guided Real-World Low-Dose CT Image Denoising—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