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 1401–1425 of 7282 papers

TitleStatusHype
CrackSegDiff: Diffusion Probability Model-based Multi-modal Crack SegmentationCode1
DART: Denoising Autoregressive Transformer for Scalable Text-to-Image Generation—0
Progressive Autoregressive Video Diffusion ModelsCode2
NetDiff: Deep Graph Denoising Diffusion for Ad Hoc Network Topology Generation—0
F5-TTS: A Fairytaler that Fakes Fluent and Faithful Speech with Flow MatchingCode11
Representation Alignment for Generation: Training Diffusion Transformers Is Easier Than You ThinkCode5
MDiff-FMT: Morphology-aware Diffusion Model for Fluorescence Molecular Tomography with Small-scale Datasets—0
MaskBlur: Spatial and Angular Data Augmentation for Light Field Image Super-ResolutionCode0
Decouple-Then-Merge: Towards Better Training for Diffusion Models—0
InstructG2I: Synthesizing Images from Multimodal Attributed GraphsCode1
Training-free Diffusion Model Alignment with Sampling DemonsCode1
IncSAR: A Dual Fusion Incremental Learning Framework for SAR Target RecognitionCode0
Think While You Generate: Discrete Diffusion with Planned DenoisingCode2
Diffusion Auto-regressive Transformer for Effective Self-supervised Time Series ForecastingCode1
PostCast: Generalizable Postprocessing for Precipitation Nowcasting via Unsupervised Blurriness Modeling—0
AP-LDM: Attentive and Progressive Latent Diffusion Model for Training-Free High-Resolution Image GenerationCode3
A noise-corrected Langevin algorithm and sampling by half-denoising—0
Towards Ultra-Low-Power Neuromorphic Speech Enhancement with Spiking-FullSubNetCode2
Image Watermarks are Removable Using Controllable Regeneration from Clean NoiseCode1
Bi-Directional MS Lesion Filling and Synthesis Using Denoising Diffusion Implicit Model-based Lesion Repainting—0
Brain Mapping with Dense Features: Grounding Cortical Semantic Selectivity in Natural Images With Vision TransformersCode0
Revealing Directions for Text-guided 3D Face Editing—0
HE-Drive: Human-Like End-to-End Driving with Vision Language Models—0
DiffuseReg: Denoising Diffusion Model for Obtaining Deformation Fields in Unsupervised Deformable Image RegistrationCode1
BrainCodec: Neural fMRI codec for the decoding of cognitive brain statesCode0
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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