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 5201–5250 of 7282 papers

TitleStatusHype
Multi-View Treelet Transform—0
Multi-wavelet residual dense convolutional neural network for image denoising—0
Equal is Not Always Fair: A New Perspective on Hyperspectral Representation Non-Uniformity—0
Mumford-Shah and Potts Regularization for Manifold-Valued Data with Applications to DTI and Q-Ball Imaging—0
Music2Latent2: Audio Compression with Summary Embeddings and Autoregressive Decoding—0
Training-free Diffusion Acceleration with Bottleneck Sampling—0
Adaptive Estimation and Learning under Temporal Distribution Shift—0
MVDD: Multi-View Depth Diffusion Models—0
MVD-Fusion: Single-view 3D via Depth-consistent Multi-view Generation—0
MVHuman: Tailoring 2D Diffusion with Multi-view Sampling For Realistic 3D Human Generation—0
Adaptive dropout for training deep neural networks—0
N2V2 -- Fixing Noise2Void Checkerboard Artifacts with Modified Sampling Strategies and a Tweaked Network Architecture—0
NAADA: A Noise-Aware Attention Denoising Autoencoder for Dental Panoramic Radiographs—0
NAN: Noise-Aware NeRFs for Burst-Denoising—0
Weakly-supervised Dictionary Learning—0
NatADiff: Adversarial Boundary Guidance for Natural Adversarial Diffusion—0
Natural Adversarial Patch Generation Method Based on Latent Diffusion Model—0
Natural Image Denoising with Convolutional Networks—0
Natural Images, Gaussian Mixtures and Dead Leaves—0
Nature Inspired Evolutionary Swarm Optimizers for Biomedical Image and Signal Processing -- A Systematic Review—0
NBD-GAP: Non-Blind Image Deblurring Without Clean Target Images—0
AW-GATCN: Adaptive Weighted Graph Attention Convolutional Network for Event Camera Data Joint Denoising and Object Recognition—0
NCSU\_SAS\_SAM: Deep Encoding and Reconstruction for Normalization of Noisy Text—0
Nearest Neighbor Search-Based Bitwise Source Separation Using Discriminant Winner-Take-All Hashing—0
Near-Field Sparse Channel Estimation for Extremely Large-Scale RIS-Aided Wireless Communications—0
Near-Infrared Coloring via a Contrast-Preserving Mapping Model—0
Near-infrared Image Deblurring and Event Denoising with Synergistic Neuromorphic Imaging—0
Near-Real-Time Mueller Polarimetric Image Processing for Neurosurgical Intervention—0
Need for Objective Task-based Evaluation of Deep Learning-Based Denoising Methods: A Study in the Context of Myocardial Perfusion SPECT—0
Needle-Match: Reliable Patch Matching Under High Uncertainty—0
Training-free Guidance in Text-to-Video Generation via Multimodal Planning and Structured Noise Initialization—0
Neighborhood filters and the decreasing rearrangement—0
Decoupled Video Generation with Chain of Training-free Diffusion Model Experts—0
Neighboring Slice Noise2Noise: Self-Supervised Medical Image Denoising from Single Noisy Image Volume—0
Recent Advances in Diffusion Models for Hyperspectral Image Processing and Analysis: A Review—0
Nested Annealed Training Scheme for Generative Adversarial Networks—0
Weakly-Supervised Speech Pre-training: A Case Study on Target Speech Recognition—0
NetDiff: Deep Graph Denoising Diffusion for Ad Hoc Network Topology Generation—0
Network Enhancement: a general method to denoise weighted biological networks—0
Network Refinement: A unified framework for enhancing signal or removing noise of networks—0
Neural Affine Grayscale Image Denoising—0
Training Stacked Denoising Autoencoders for Representation Learning—0
Neural Cell Video Synthesis via Optical-Flow Diffusion—0
Neural Compression-Based Feature Learning for Video Restoration—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
Neural Empirical Bayes—0
Neural information coding for efficient spike-based image denoising—0
Neural Inverse Scattering with Score-based Regularization—0
Neural Knitworks: Patched Neural Implicit Representation Networks—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