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 4326–4350 of 7282 papers

TitleStatusHype
PURR: Efficiently Editing Language Model Hallucinations by Denoising Language Model Corruptions—0
Denoising Bottleneck with Mutual Information Maximization for Video Multimodal FusionCode0
Least Squares Regression Can Exhibit Under-Parameterized Double Descent—0
Textless Speech-to-Speech Translation With Limited Parallel DataCode0
Masked Modeling Duo for Speech: Specializing General-Purpose Audio Representation to Speech using Denoising Distillation—0
DiffHand: End-to-End Hand Mesh Reconstruction via Diffusion Models—0
MOTRv3: Release-Fetch Supervision for End-to-End Multi-Object Tracking—0
Realistic Noise Synthesis with Diffusion ModelsCode0
When Does Monolingual Data Help Multilingual Translation: The Role of Domain and Model Scale—0
A Dive into SAM Prior in Image Restoration—0
Basis Pursuit Denoising via Recurrent Neural Network Applied to Super-resolving SAR Tomography—0
Sparsity and Coefficient Permutation Based Two-Domain AMP for Image Block Compressed Sensing—0
Why current rain denoising models fail on CycleGAN created rain images in autonomous driving—0
ViT-TTS: Visual Text-to-Speech with Scalable Diffusion Transformer—0
Extrapolating Multilingual Understanding Models as Multilingual Generators—0
DCCRN-KWS: an audio bias based model for noise robust small-footprint keyword spotting—0
DiffUCD:Unsupervised Hyperspectral Image Change Detection with Semantic Correlation Diffusion Model—0
Guided Motion Diffusion for Controllable Human Motion Synthesis—0
Moment Matching Denoising Gibbs SamplingCode0
A quality assurance framework for real-time monitoring of deep learning segmentation models in radiotherapy—0
The probability flow ODE is provably fast—0
ISP meets Deep Learning: A Survey on Deep Learning Methods for Image Signal Processing—0
SIDAR: Synthetic Image Dataset for Alignment & RestorationCode0
NODE-ImgNet: a PDE-informed effective and robust model for image denoisingCode0
Brain Imaging-to-Graph Generation using Adversarial Hierarchical Diffusion Models for MCI Causality Analysis—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