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 6476–6500 of 7282 papers

TitleStatusHype
Massive Styles Transfer with Limited Labeled DataCode0
Self-Supervised Audio-and-Text Pre-training with Extremely Low-Resource Parallel DataCode0
DiffMotion: Speech-Driven Gesture Synthesis Using Denoising Diffusion ModelCode0
When SparseMoE Meets Noisy Interactions: An Ensemble View on Denoising RecommendationCode0
DiffNorm: Self-Supervised Normalization for Non-autoregressive Speech-to-speech TranslationCode0
Learned Convolutional Sparse CodingCode0
TC-PDM: Temporally Consistent Patch Diffusion Models for Infrared-to-Visible Video TranslationCode0
Learned D-AMP: Principled Neural Network based Compressive Image RecoveryCode0
DiffPAD: Denoising Diffusion-based Adversarial Patch DecontaminationCode0
Generating Classification Weights with GNN Denoising Autoencoders for Few-Shot LearningCode0
Cycle Consistent Adversarial Denoising Network for Multiphase Coronary CT AngiographyCode0
Source Camera Identification with Multi-Scale Feature Fusion NetworkCode0
Uncertainty Quantification for Hyperspectral Image Denoising Frameworks based on Low-rank Matrix ApproximationCode0
Deep Hyperspectral Prior: Denoising, Inpainting, Super-ResolutionCode0
Self-Supervised Deep Depth DenoisingCode0
High-Quality Self-Supervised Deep Image DenoisingCode0
Deep Perceptual Enhancement for Medical Image AnalysisCode0
Assessing The Impact of CNN Auto Encoder-Based Image Denoising on Image Classification TasksCode0
STAR-Net: An Interpretable Model-Aided Network for Remote Sensing Image DenoisingCode0
Times series averaging and denoising from a probabilistic perspective on time-elastic kernelsCode0
Generating observation guided ensembles for data assimilation with denoising diffusion probabilistic modelCode0
Unified Generation, Reconstruction, and Representation: Generalized Diffusion with Adaptive Latent Encoding-DecodingCode0
Generating symbolic music using diffusion modelsCode0
Accurate Segmentation of Optic Disc And Cup from Multiple Pseudo-labels by Noise-aware LearningCode0
Residual Dense Network for Image RestorationCode0
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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