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 4126–4150 of 7282 papers

TitleStatusHype
Statistically unbiased prediction enables accurate denoising of voltage imaging dataCode1
A Structure-Guided Diffusion Model for Large-Hole Image CompletionCode0
Patch-Based Denoising Diffusion Probabilistic Model for Sparse-View CT Reconstruction—0
Iterative execution of discrete and inverse discrete Fourier transforms with applications for signal denoising via sparsification—0
RenderDiffusion: Image Diffusion for 3D Reconstruction, Inpainting and GenerationCode2
Patch-Craft Self-Supervised Training for Correlated Image DenoisingCode0
Proactively Predicting Dynamic 6G Link Blockages Using LiDAR and In-Band Signatures—0
DiffusionDet: Diffusion Model for Object DetectionCode4
EmoDiff: Intensity Controllable Emotional Text-to-Speech with Soft-Label Guidance—0
Super-resolution Reconstruction of Single Image for Latent features—0
Learning to Kindle the StarlightCode0
CaDM: Codec-aware Diffusion Modeling for Neural-enhanced Video Streaming—0
N2V2 -- Fixing Noise2Void Checkerboard Artifacts with Modified Sampling Strategies and a Tweaked Network Architecture—0
Diffusion Models for Medical Image Analysis: A Comprehensive SurveyCode4
Denoising diffusion models for out-of-distribution detectionCode1
CurvPnP: Plug-and-play Blind Image Restoration with Deep Curvature DenoiserCode0
Self-Supervised Image Restoration with Blurry and Noisy PairsCode1
A Novel Sampling Scheme for Text- and Image-Conditional Image Synthesis in Quantized Latent SpacesCode2
TIER-A: Denoising Learning Framework for Information Extraction—0
A Tale of Two Graphs: Freezing and Denoising Graph Structures for Multimodal RecommendationCode1
Point-DAE: Denoising Autoencoders for Self-supervised Point Cloud LearningCode1
JSRNN: Joint Sampling and Reconstruction Neural Networks for High Quality Image Compressed Sensing—0
An Adapter based Multi-label Pre-training for Speech Separation and Enhancement—0
A Comprehensive Survey of Transformers for Computer Vision—0
Multiresolution Dual-Polynomial Decomposition Approach for Optimized Characterization of Motor Intent in Myoelectric Control Systems—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