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 3976–4000 of 7282 papers

TitleStatusHype
MRI Recovery with A Self-calibrated Denoiser—0
MRIR: Integrating Multimodal Insights for Diffusion-based Realistic Image Restoration—0
MS-UNet-v2: Adaptive Denoising Method and Training Strategy for Medical Image Segmentation with Small Training Data—0
TQD-Track: Temporal Query Denoising for 3D Multi-Object Tracking—0
Mu^2SLAM: Multitask, Multilingual Speech and Language Models—0
Adaptive Extensions of Unbiased Risk Estimators for Unsupervised Magnetic Resonance Image Denoising—0
Multi-Architecture Multi-Expert Diffusion Models—0
Multi-band Weighted l_p Norm Minimization for Image Denoising—0
Multi-bin Trainable Linear Unit for Fast Image Restoration Networks—0
Multibranch Generative Models for Multichannel Imaging with an Application to PET/CT Synergistic Reconstruction—0
Multi-channel Nuclear Norm Minus Frobenius Norm Minimization for Color Image Denoising—0
Multi-Channel Speech Denoising for Machine Ears—0
Multichannel Speech Enhancement by Raw Waveform-mapping using Fully Convolutional Networks—0
Multi-Channel Swin Transformer Framework for Bearing Remaining Useful Life Prediction—0
Multi-channel Weighted Nuclear Norm Minimization for Real Color Image Denoising—0
MCDIP-ADMM: Overcoming Overfitting in DIP-based CT reconstruction—0
Multi-Conditioned Denoising Diffusion Probabilistic Model (mDDPM) for Medical Image Synthesis—0
Multi-Contextual Design of Convolutional Neural Network for Steganalysis—0
Multi-Cycle-Consistent Adversarial Networks for CT Image Denoising—0
Multi-Cycle-Consistent Adversarial Networks for Edge Denoising of Computed Tomography Images—0
Multi-Decoder Networks with Multi-Denoising Inputs for Tumor Segmentation—0
Multi-Dimensional Framework for EEG Signal Processing and Denoising Through Tensor-based Architecture—0
Multidimensional TV-Stokes for image processing—0
TRACE: Trajectory-Constrained Concept Erasure in Diffusion Models—0
Multi-Domain Processing via Hybrid Denoising Networks for Speech Enhancement—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