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 776–800 of 7282 papers

TitleStatusHype
SigWavNet: Learning Multiresolution Signal Wavelet Network for Speech Emotion RecognitionCode1
TRADES: Generating Realistic Market Simulations with Diffusion ModelsCode2
Spend Wisely: Maximizing Post-Training Gains in Iterative Synthetic Data BoostrappingCode0
GestureLSM: Latent Shortcut based Co-Speech Gesture Generation with Spatial-Temporal ModelingCode2
Collaborative Diffusion Model for Recommender System—0
Ambient Denoising Diffusion Generative Adversarial Networks for Establishing Stochastic Object Models from Noisy Image Data—0
Beyond Fixed Horizons: A Theoretical Framework for Adaptive Denoising Diffusions—0
RGB-Event ISP: The Dataset and BenchmarkCode1
PSyDUCK: Training-Free Steganography for Latent Diffusion—0
Principal Components for Neural Network InitializationCode0
CoSTI: Consistency Models for (a faster) Spatio-Temporal ImputationCode0
Inference-Time Text-to-Video Alignment with Diffusion Latent Beam SearchCode1
RMDM: Radio Map Diffusion Model with Physics InformedCode1
From Data to Action: Charting A Data-Driven Path to Combat Antimicrobial Resistance—0
Free-T2M: Frequency Enhanced Text-to-Motion Diffusion Model With Consistency LossCode2
Task-based Regularization in Penalized Least-Squares for Binary Signal Detection Tasks in Medical Image Denoising—0
Music2Latent2: Audio Compression with Summary Embeddings and Autoregressive Decoding—0
SAeUron: Interpretable Concept Unlearning in Diffusion Models with Sparse AutoencodersCode2
Distinguished Quantized Guidance for Diffusion-based Sequence Recommendation—0
RadioLLM: Introducing Large Language Model into Cognitive Radio via Hybrid Prompt and Token ReprogrammingsCode1
MR imaging in the low-field: Leveraging the power of machine learning—0
Variational Schrödinger Momentum Diffusion—0
Decrypting the temperature field in flow boiling with latent diffusion models—0
The Effect of Optimal Self-Distillation in Noisy Gaussian Mixture ModelCode0
ARFlow: Autogressive Flow with Hybrid Linear Attention—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