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 726–750 of 7282 papers

TitleStatusHype
Diffusion Policies creating a Trust Region for Offline Reinforcement LearningCode1
DiffO: Single-step Diffusion for Image Compression at Ultra-Low BitratesCode1
A Flexible Framework for Designing Trainable Priors with Adaptive Smoothing and Game EncodingCode1
DermoSegDiff: A Boundary-aware Segmentation Diffusion Model for Skin Lesion DelineationCode1
Diffusion Priors for Variational Likelihood Estimation and Image DenoisingCode1
Designing and Training of A Dual CNN for Image DenoisingCode1
DenoSent: A Denoising Objective for Self-Supervised Sentence Representation LearningCode1
DenoMamba: A fused state-space model for low-dose CT denoisingCode1
Diffusion-SS3D: Diffusion Model for Semi-supervised 3D Object DetectionCode1
DeSTSeg: Segmentation Guided Denoising Student-Teacher for Anomaly DetectionCode1
Denoising Relation Extraction from Document-level Distant SupervisionCode1
Diffusive Gibbs SamplingCode1
DIFFVSGG: Diffusion-Driven Online Video Scene Graph GenerationCode1
Denoising Point Clouds in Latent Space via Graph Convolution and Invertible Neural NetworkCode1
Denoising Task Routing for Diffusion ModelsCode1
4D Facial Expression Diffusion ModelCode1
Digital Gimbal: End-to-end Deep Image Stabilization with Learnable Exposure TimesCode1
A Variational Perspective on Solving Inverse Problems with Diffusion ModelsCode1
Discovering Interpretable Directions in the Semantic Latent Space of Diffusion ModelsCode1
ASCON: Anatomy-aware Supervised Contrastive Learning Framework for Low-dose CT DenoisingCode1
Denoising of 3D MR images using a voxel-wise hybrid residual MLP-CNN model to improve small lesion diagnostic confidenceCode1
DETA: Denoised Task Adaptation for Few-Shot LearningCode1
Denoising Masked AutoEncoders Help Robust ClassificationCode1
Denoising Lévy Probabilistic ModelsCode1
Denoising MCMC for Accelerating Diffusion-Based Generative ModelsCode1
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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