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 3051–3100 of 7282 papers

TitleStatusHype
Bayesian Cramér-Rao Bound Estimation with Score-Based Models—0
DeepPCR: Parallelizing Sequential Operations in Neural Networks—0
Object Motion Guided Human Motion Synthesis—0
Distilling ODE Solvers of Diffusion Models into Smaller Steps—0
Neuromorphic Imaging with Joint Image Deblurring and Event Denoising—0
Uncertainty Quantification via Neural Posterior Principal Components—0
High Perceptual Quality Wireless Image Delivery with Denoising Diffusion ModelsCode1
Factorized Diffusion Architectures for Unsupervised Image Generation and Segmentation—0
Guided Frequency Loss for Image Restoration—0
Wave-shape Function Model Order Estimation by Trigonometric Regression—0
Fully Adaptive Time-Varying Wave-Shape Model: Applications in Biomedical Signal ProcessingCode0
Joint Prediction and Denoising for Large-scale Multilingual Self-supervised Learning—0
An Ensemble Model for Distorted Images in Real Scenarios—0
Bootstrap Diffusion Model Curve Estimation for High Resolution Low-Light Image Enhancement—0
DONNAv2 -- Lightweight Neural Architecture Search for Vision tasks—0
Image Denoising via Style Disentanglement—0
Soft Mixture Denoising: Beyond the Expressive Bottleneck of Diffusion Models—0
On the Posterior Distribution in Denoising: Application to Uncertainty QuantificationCode1
Connecting Image Inpainting with Denoising in the Homogeneous Diffusion Setting—0
DurIAN-E: Duration Informed Attention Network For Expressive Text-to-Speech Synthesis—0
Training Your Image Restoration Network Better with Random Weight Network as Optimization Function—0
Training Your Image Restoration Network Better with Random Weight Network as Optimization Function—0
Light Field Diffusion for Single-View Novel View Synthesis—0
PSDiff: Diffusion Model for Person Search with Iterative and Collaborative Refinement—0
Score Mismatching for Generative ModelingCode1
Deep Networks as Denoising Algorithms: Sample-Efficient Learning of Diffusion Models in High-Dimensional Graphical Models—0
FreeU: Free Lunch in Diffusion U-NetCode3
CoT-BERT: Enhancing Unsupervised Sentence Representation through Chain-of-ThoughtCode1
Reconstruct-and-Generate Diffusion Model for Detail-Preserving Image Denoising—0
ConsistencyTTA: Accelerating Diffusion-Based Text-to-Audio Generation with Consistency DistillationCode1
Towards Generative Modeling of Urban Flow through Knowledge-enhanced Denoising DiffusionCode1
Assessing the capacity of a denoising diffusion probabilistic model to reproduce spatial context—0
Self2Seg: Single-Image Self-Supervised Joint Segmentation and DenoisingCode0
Mixed Graph Signal Analysis of Joint Image Denoising / InterpolationCode0
Speech Synthesis By Unrolling Diffusion Process using Neural Network Layers—0
Joint Demosaicing and Denoising with Double Deep Image Priors—0
Gradpaint: Gradient-Guided Inpainting with Diffusion Models—0
Single and Few-step Diffusion for Generative Speech EnhancementCode1
All-optical image denoising using a diffractive visual processor—0
Continuous Modeling of the Denoising Process for Speech Enhancement Based on Deep Learning—0
Wavelet-based Topological Loss for Low-Light Image Denoising—0
Dual-Camera Joint Deblurring-Denoising—0
Large Intestine 3D Shape Refinement Using Point Diffusion Models for Digital Phantom Generation—0
A Unified View Between Tensor Hypergraph Neural Networks And Signal Denoising—0
Hyperspectral Image Denoising via Self-Modulating Convolutional Neural NetworksCode0
Cartoondiff: Training-free Cartoon Image Generation with Diffusion Transformer Models—0
Masked Diffusion with Task-awareness for Procedure Planning in Instructional VideosCode0
Indoor Scene Reconstruction with Fine-Grained Details Using Hybrid Representation and Normal Prior EnhancementCode1
Complexity Scaling for Speech Denoising—0
Beta DiffusionCode1
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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