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 3726–3750 of 7282 papers

TitleStatusHype
Efficient View Synthesis and 3D-based Multi-Frame Denoising with Multiplane Feature RepresentationsCode1
Steered Mixture of Experts Regression for Image Denoising with Multi-Model-Inference—0
Masked Autoencoders as Image ProcessorsCode1
DDP: Diffusion Model for Dense Visual PredictionCode2
HDR Imaging with Spatially Varying Signal-to-Noise Ratios—0
Diffusion Schrödinger Bridge Matching—0
The G-invariant graph Laplacian—0
Exploring Efficient Asymmetric Blind-Spots for Self-Supervised Denoising in Real-World Scenarios—0
Unlocking Masked Autoencoders as Loss Function for Image and Video Restoration—0
Implicit Diffusion Models for Continuous Super-ResolutionCode2
Real-time Controllable Denoising for Image and VideoCode1
4D Facial Expression Diffusion ModelCode1
WordStylist: Styled Verbatim Handwritten Text Generation with Latent Diffusion ModelsCode1
Robust Andrew's sine estimate adaptive filtering—0
Channel Phase Processing in Wireless Networks for Human Activity Recognition—0
GeoTMI:Predicting quantum chemical property with easy-to-obtain geometry via positional denoising—0
Denoising Autoencoder-based Defensive Distillation as an Adversarial Robustness Algorithm—0
Whole-body PET image denoising for reduced acquisition time—0
DDMM-Synth: A Denoising Diffusion Model for Cross-modal Medical Image Synthesis with Sparse-view Measurement Embedding—0
Pushing The Limits of the Wiener Filter in Image DenoisingCode0
Seer: Language Instructed Video Prediction with Latent Diffusion ModelsCode1
Memory-Efficient 3D Denoising Diffusion Models for Medical Image ProcessingCode1
Spatially Adaptive Self-Supervised Learning for Real-World Image DenoisingCode1
Exploring Continual Learning of Diffusion Models—0
Generalizable Denoising of Microscopy Images using Generative Adversarial Networks and Contrastive Learning—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