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 1226–1250 of 7282 papers

TitleStatusHype
SoundSil-DS: Deep Denoising and Segmentation of Sound-field Images with SilhouettesCode0
MRI denoising with a non-blind deep complex-valued convolutional neural network—0
Efficient Denoising Method to Improve The Resolution of Satellite Images—0
White-Box Diffusion Transformer for single-cell RNA-seq generationCode0
PLM-Based Discrete Diffusion Language Models with Entropy-Adaptive Gibbs Sampling—0
DDIM-Driven Coverless Steganography Scheme with Real Key—0
An ambient denoising method based on multi-channel non-negative matrix factorization for wheezing detection—0
Adaptive Whole-Body PET Image Denoising Using 3D Diffusion Models with ControlNet—0
UnDIVE: Generalized Underwater Video Enhancement Using Generative PriorsCode0
Untrained Perceptual Loss for image denoising of line-like structures in MR images—0
Nonperfused Retinal Capillaries -- A New Method Developed on OCT and OCTA—0
MVSplat360: Feed-Forward 360 Scene Synthesis from Sparse ViewsCode3
DimensionX: Create Any 3D and 4D Scenes from a Single Image with Controllable Video Diffusion—0
LoFi: Neural Local Fields for Scalable Image ReconstructionCode0
Diff-2-in-1: Bridging Generation and Dense Perception with Diffusion Models—0
Quantum Diffusion Models for Few-Shot Learning—0
PocoLoco: A Point Cloud Diffusion Model of Human Shape in Loose ClothingCode1
ADMIRE: a locally adaptive single-image, non-uniformity correction and denoising algorithm: application to uncooled IR camera—0
SynthSet: Generative Diffusion Model for Semantic Segmentation in Precision AgricultureCode0
Two-Stage Pretraining for Molecular Property Prediction in the Wild—0
Gradient-Guided Conditional Diffusion Models for Private Image Reconstruction: Analyzing Adversarial Impacts of Differential Privacy and Denoising—0
Adaptive Caching for Faster Video Generation with Diffusion Transformers—0
On the phase diagram of extensive-rank symmetric matrix denoising beyond rotational invariance—0
Entropy stable conservative flux form neural networks—0
Robust plug-and-play methods for highly accelerated non-Cartesian MRI reconstruction—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