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 3851–3875 of 7282 papers

TitleStatusHype
Mesh Denoising based on Normal Voting Tensor and Binary Optimization—0
Mesh Denoising Transformer—0
Mesh Denoising via Cascaded Normal Regression—0
MeshUp: Multi-Target Mesh Deformation via Blended Score Distillation—0
MetaDiff: Meta-Learning with Conditional Diffusion for Few-Shot Learning—0
MetaDIP: Accelerating Deep Image Prior with Meta Learning—0
MetaEarth: A Generative Foundation Model for Global-Scale Remote Sensing Image Generation—0
Meta Ensemble for Japanese-Chinese Neural Machine Translation: Kyoto-U+ECNU Participation to WAT 2020—0
Meta-Learn Unimodal Signals with Weak Supervision for Multimodal Sentiment Analysis—0
Meta-Optimization of Deep CNN for Image Denoising Using LSTM—0
Method for motion artifact reduction using a convolutional neural network for dynamic contrast enhanced MRI of the liver—0
Metric Convolutions: A Unifying Theory to Adaptive Convolutions—0
METRO: Efficient Denoising Pretraining of Large Scale Autoencoding Language Models with Model Generated Signals—0
Metropolis Sampling for Constrained Diffusion Models—0
MFR-Net: Multi-faceted Responsive Listening Head Generation via Denoising Diffusion Model—0
MFSR: Multi-fractal Feature for Super-resolution Reconstruction with Fine Details Recovery—0
MICAS: Multi-grained In-Context Adaptive Sampling for 3D Point Cloud Processing—0
Microarrays denoising via smoothing of coefficients in wavelet domain—0
Adaptive Regularized Low-Rank Tensor Decomposition for Hyperspectral Image Denoising and Destriping—0
Towards more transferable adversarial attack in black-box manner—0
Microseismic events enhancement and detection in sensor arrays using autocorrelation based filtering—0
Weakly-supervised Audio-visual Sound Source Detection and Separation—0
Towards Precise Embodied Dialogue Localization via Causality Guided Diffusion—0
MIEHDR CNN: Main Image Enhancement based Ghost-Free High Dynamic Range Imaging using Dual-Lens Systems—0
MIGA: Mutual Information-Guided Attack on Denoising Models for Semantic Manipulation—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