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 3151–3175 of 7282 papers

TitleStatusHype
Enhancement of the Prefiltered Rotationally Invariant Non-local PCA Algorithm for MRI—0
Multi-plane denoising diffusion-based dimensionality expansion for 2D-to-3D reconstruction of microstructures with harmonized sampling—0
Label Denoising through Cross-Model Agreement—0
DiffI2I: Efficient Diffusion Model for Image-to-Image Translation—0
Sparse Models for Machine Learning—0
Residual Denoising Diffusion ModelsCode2
Region-Disentangled Diffusion Model for High-Fidelity PPG-to-ECG TranslationCode1
Distribution-Aligned Diffusion for Human Mesh Recovery—0
May the Force be with You: Unified Force-Centric Pre-Training for 3D Molecular Conformations—0
Head-Neck Dual-energy CT Contrast Media Reduction Using Diffusion Models—0
Full-dose Whole-body PET Synthesis from Low-dose PET Using High-efficiency Denoising Diffusion Probabilistic Model: PET Consistency ModelCode1
Learning to Decouple and Generate Seismic Random Noise via Invertible Neural NetworkCode0
High-quality Image Dehazing with Diffusion ModelCode1
InverseSR: 3D Brain MRI Super-Resolution Using a Latent Diffusion ModelCode1
Efficient Transfer Learning in Diffusion Models via Adversarial Noise—0
DISGAN: Wavelet-informed Discriminator Guides GAN to MRI Super-resolution with Noise CleaningCode0
Diffusion-based Image Translation with Label Guidance for Domain Adaptive Semantic Segmentation—0
Expressive probabilistic sampling in recurrent neural networksCode0
SDeMorph: Towards Better Facial De-morphing from Single Morph—0
Regularization by Neural Style Transfer for MRI Field-Transfer Reconstruction with Limited DataCode0
Diffusion Model as Representation LearnerCode1
Texture Generation on 3D Meshes with Point-UV DiffusionCode2
Adaptive Uncertainty-Guided Model Selection for Data-Driven PDE DiscoveryCode0
Semi-Implicit Variational Inference via Score MatchingCode0
Semantic-Human: Neural Rendering of Humans from Monocular Video with Human Parsing—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