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 3901–3925 of 7282 papers

TitleStatusHype
MixerMDM: Learnable Composition of Human Motion Diffusion Models—0
Mixing-Denoising Generalizable Occupancy Networks—0
Mixture-Net: Low-Rank Deep Image Prior Inspired by Mixture Models for Spectral Image Recovery—0
Mixture of Efficient Diffusion Experts Through Automatic Interval and Sub-Network Selection—0
Mixture of Robust Experts (MoRE):A Robust Denoising Method towards multiple perturbations—0
Towards speech enhancement using a variational U-Net architecture—0
MMAR: Towards Lossless Multi-Modal Auto-Regressive Probabilistic Modeling—0
Towards stationary time-vertex signal processing—0
MMO-IG: Multi-Class and Multi-Scale Object Image Generation for Remote Sensing—0
MMSE Estimation for Poisson Noise Removal in Images—0
Adaptive noise imitation for image denoising—0
Mobile Video Diffusion—0
Global Adaptive Filtering Layer for Computer Vision—0
Towards the Automation of Deep Image Prior—0
Modality-Independent Explainable Detection of Inaccurate Organ Segmentations Using Denoising Autoencoders—0
Model-Based Deep Learning for Reconstruction of Joint k-q Under-sampled High Resolution Diffusion MRI—0
Model-Based Image Signal Processors via Learnable Dictionaries—0
Towards Transferable Speech Emotion Representation: On loss functions for cross-lingual latent representations—0
Towards Transformer-Based Aligned Generation with Self-Coherence Guidance—0
Modeling documents with Generative Adversarial Networks—0
CRISP: Clustering Multi-Vector Representations for Denoising and Pruning—0
Modeling Graph Node Correlations with Neighbor Mixture Models—0
Modeling Pedestrian Intrinsic Uncertainty for Multimodal Stochastic Trajectory Prediction via Energy Plan Denoising—0
Modeling sRGB Camera Noise with Normalizing Flows—0
Modeling Temporal Data as Continuous Functions with Stochastic Process Diffusion—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