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 3976–4000 of 7282 papers

TitleStatusHype
Point Cloud Denoising and Outlier Detection with Local Geometric Structure by Dynamic Graph CNN—0
Echocardiography video synthesis from end diastolic semantic map via diffusion model—0
Kronecker-structured Sparse Vector Recovery with Application to IRS-MIMO Channel EstimationCode0
Crowd Counting in Harsh Weather using Image Denoising with Pix2Pix GANs—0
Psychoacoustic Challenges Of Speech Enhancement On VoIP Platforms—0
Diffusion Models for Wireless Communications—0
Diffusion Prior Regularized Iterative Reconstruction for Low-dose CT—0
Stochastic Super-resolution of Cosmological Simulations with Denoising Diffusion Models—0
ViTs are Everywhere: A Comprehensive Study Showcasing Vision Transformers in Different Domain—0
Layout Sequence Prediction From Noisy Mobile Modality—0
Integration-free Training for Spatio-temporal Multimodal Covariate Deep Kernel Point Processes—0
Super Denoise Net: Speech Super Resolution with Noise Cancellation in Low Sampling Rate Noisy Environments—0
Transforming Pixels into a Masterpiece: AI-Powered Art Restoration using a Novel Distributed Denoising CNN (DDCNN)—0
Image Compression and Decompression Framework Based on Latent Diffusion Model for Breast MammographyCode0
DiffNAS: Bootstrapping Diffusion Models by Prompting for Better Architectures—0
Multi-objective Progressive Clustering for Semi-supervised Domain Adaptation in Speaker Verification—0
Observation-Guided Diffusion Probabilistic ModelsCode0
Certification of Deep Learning Models for Medical Image SegmentationCode0
ACT-Net: Anchor-context Action Detection in Surgery Videos—0
Efficient Video and Audio processing with Loihi 2—0
Analysis of learning a flow-based generative model from limited sample complexityCode0
A Complementary Global and Local Knowledge Network for Ultrasound denoising with Fine-grained RefinementCode0
RL-based Stateful Neural Adaptive Sampling and Denoising for Real-Time Path TracingCode0
Magicremover: Tuning-free Text-guided Image inpainting with Diffusion ModelsCode0
Ophiuchus: Scalable Modeling of Protein Structures through Hierarchical Coarse-graining SO(3)-Equivariant Autoencoders—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