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 1576–1600 of 7282 papers

TitleStatusHype
WaveTransfer: A Flexible End-to-end Multi-instrument Timbre Transfer with Diffusion—0
Empirical Bayesian image restoration by Langevin sampling with a denoising diffusion implicit prior—0
Generating High Dimensional User-Specific Wireless Channels using Diffusion Models—0
aTENNuate: Optimized Real-time Speech Enhancement with Deep SSMs on Raw Audio—0
DKDM: Data-Free Knowledge Distillation for Diffusion Models with Any ArchitectureCode1
MADiff: Motion-Aware Mamba Diffusion Models for Hand Trajectory Prediction on Egocentric Videos—0
Reliable Deep Diffusion Tensor Estimation: Rethinking the Power of Data-Driven Optimization Routine—0
Diffusion Models Learn Low-Dimensional Distributions via Subspace ClusteringCode1
HiPrompt: Tuning-free Higher-Resolution Generation with Hierarchical MLLM PromptsCode1
StyleTokenizer: Defining Image Style by a Single Instance for Controlling Diffusion ModelsCode2
LinFusion: 1 GPU, 1 Minute, 16K ImageCode3
TimeDiT: General-purpose Diffusion Transformers for Time Series Foundation Model—0
PatternPaint: Practical Layout Pattern Generation Using Diffusion-Based Inpainting—0
3D Priors-Guided Diffusion for Blind Face RestorationCode1
Optimal training of finitely-sampled quantum reservoir computers for forecasting of chaotic dynamics—0
Misaligned Over-The-Air Computation of Multi-Sensor Data with Wiener-Denoiser NetworkCode0
McCaD: Multi-Contrast MRI Conditioned, Adaptive Adversarial Diffusion Model for High-Fidelity MRI Synthesis—0
MoManifold: Learning to Measure 3D Human Motion via Decoupled Joint Acceleration Manifolds—0
Diffusion based multi-domain neuroimaging harmonization method with preservation of anatomical details—0
Self-supervised learning for crystal property prediction via denoising—0
RISSOLE: Parameter-efficient Diffusion Models via Block-wise Generation and Retrieval-Guidance—0
Approximately Invertible Neural Network for Learned Image Compression—0
Enabling Local Editing in Diffusion Models by Joint and Individual Component Analysis—0
RLCP: A Reinforcement Learning-based Copyright Protection Method for Text-to-Image Diffusion Model—0
Enhanced Control for Diffusion Bridge in Image RestorationCode0
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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