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 6951–6975 of 7282 papers

TitleStatusHype
Automated Clustering of High-dimensional Data with a Feature Weighted Mean Shift AlgorithmCode0
DeepISP: Towards Learning an End-to-End Image Processing PipelineCode0
BSS-CFFMA: Cross-Domain Feature Fusion and Multi-Attention Speech Enhancement Network based on Self-Supervised EmbeddingCode0
PROUD: PaRetO-gUided Diffusion Model for Multi-objective GenerationCode0
Deep sound-field denoiser: optically-measured sound-field denoising using deep neural networkCode0
Defending Observation Attacks in Deep Reinforcement Learning via Detection and DenoisingCode0
Make Some Noise: Unlocking Language Model Parallel Inference Capability through Noisy TrainingCode0
Efficient First-Order Algorithms for Adaptive Signal DenoisingCode0
Efficient graph construction for image representationCode0
Robust Representation Consistency Model via Contrastive DenoisingCode0
Robust Segmentation of Brain MRI in the Wild with Hierarchical CNNs and no RetrainingCode0
On Analyzing Generative and Denoising Capabilities of Diffusion-based Deep Generative ModelsCode0
Efficient Manifold and Subspace Approximations with SphereletsCode0
Clean self-supervised MRI reconstruction from noisy, sub-sampled training data with Robust SSDUCode0
Supervised and Unsupervised Speech Enhancement Using Nonnegative Matrix FactorizationCode0
Contrast-augmented Diffusion Model with Fine-grained Sequence Alignment for Markup-to-Image GenerationCode0
MambaFoley: Foley Sound Generation using Selective State-Space ModelsCode0
Multi-Stage Speaker Diarization for Noisy ClassroomsCode0
Efficient Off-Grid Bayesian Parameter Estimation for Kronecker-Structured SignalsCode0
Building 3D In-Context Learning Universal Model in NeuroimagingCode0
Side Window FilteringCode0
Efficient Semantic Diffusion Architectures for Model Training on Synthetic EchocardiogramsCode0
Annotated Biomedical Video Generation using Denoising Diffusion Probabilistic Models and Flow FieldsCode0
Color Image Restoration Exploiting Inter-channel Correlation with a 3-stage CNNCode0
Robust Tiny Object Detection in Aerial Images amidst Label NoiseCode0
Show:102550
← PrevPage 279 of 292Next →
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