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 3451–3475 of 7282 papers

TitleStatusHype
Microscopy image reconstruction with physics-informed denoising diffusion probabilistic modelCode1
Enhancing Point Annotations with Superpixel and Confidence Learning Guided for Improving Semi-Supervised OCT Fluid Segmentation—0
Perceptual Kalman Filters: Online State Estimation under a Perfect Perceptual-Quality Constraint—0
DiffECG: A Versatile Probabilistic Diffusion Model for ECG Signals Synthesis—0
The Surprising Effectiveness of Diffusion Models for Optical Flow and Monocular Depth Estimation—0
Unlearnable Examples for Diffusion Models: Protect Data from Unauthorized Exploitation—0
Fast and Interpretable Nonlocal Neural Networks for Image Denoising via Group-Sparse Convolutional Dictionary LearningCode0
Denoising Diffusion Semantic Segmentation with Mask Prior Modeling—0
Bilevel Fast Scene Adaptation for Low-Light Image EnhancementCode1
Invisible Image Watermarks Are Provably Removable Using Generative AICode2
Learning Signed Distance Functions from Noisy 3D Point Clouds via Noise to Noise MappingCode1
Addressing Negative Transfer in Diffusion Models—0
SnapFusion: Text-to-Image Diffusion Model on Mobile Devices within Two Seconds—0
DiffPack: A Torsional Diffusion Model for Autoregressive Protein Side-Chain PackingCode1
Low-Light Image Enhancement with Wavelet-based Diffusion ModelsCode2
ACLM: A Selective-Denoising based Generative Data Augmentation Approach for Low-Resource Complex NERCode1
Protein Design with Guided Discrete DiffusionCode1
SafeDiffuser: Safe Planning with Diffusion Probabilistic Models—0
Synthetic CT Generation from MRI using 3D Transformer-based Denoising Diffusion ModelCode1
A Unified Conditional Framework for Diffusion-based Image Restoration—0
A Geometric Perspective on Diffusion ModelsCode2
Improving Handwritten OCR with Training Samples Generated by Glyph Conditional Denoising Diffusion Probabilistic Model—0
A Heat Diffusion Perspective on Geodesic Preserving Dimensionality ReductionCode0
Large Car-following Data Based on Lyft level-5 Open Dataset: Following Autonomous Vehicles vs. Human-driven Vehicles—0
DyGen: Learning from Noisy Labels via Dynamics-Enhanced Generative ModelingCode1
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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