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 4201–4250 of 7282 papers

TitleStatusHype
A Magnetic Framelet-Based Convolutional Neural Network for Directed Graphs—0
Speech Dereverberation with a Reverberation Time Shortening TargetCode0
Representation Learning with Diffusion ModelsCode1
Improving Chinese Story Generation via Awareness of Syntactic Dependencies and SemanticsCode0
Video super-resolution for single-photon LIDAR—0
Efficient Bi-Level Optimization for Recommendation DenoisingCode0
Denoising Enhanced Distantly Supervised Ultrafine Entity Typing—0
BirdSoundsDenoising: Deep Visual Audio Denoising for Bird SoundsCode1
Signal Processing for Implicit Neural Representations—0
spatial-dccrn: dccrn equipped with frame-level angle feature and hybrid filtering for multi-channel speech enhancement—0
Scale-Agnostic Super-Resolution in MRI using Feature-Based Coordinate Networks—0
Gated Recurrent Unit for Video Denoising—0
Robust Graph Filter Identification and Graph Denoising from Signal ObservationsCode0
TransFusion: Transcribing Speech with Multinomial DiffusionCode1
Adv-Attribute: Inconspicuous and Transferable Adversarial Attack on Face Recognition—0
Dissipative residual layers for unsupervised implicit parameterization of data manifolds—0
LION: Latent Point Diffusion Models for 3D Shape GenerationCode2
Common Corruption Robustness of Point Cloud Detectors: Benchmark and Enhancement—0
3D Brain and Heart Volume Generative Models: A SurveyCode1
Unsupervised classification of the spectrogram zerosCode0
Evaluating Unsupervised Denoising Requires Unsupervised MetricsCode0
GENIE: Higher-Order Denoising Diffusion SolversCode1
Instance Regularization for Discriminative Language Model Pre-trainingCode0
Markup-to-Image Diffusion Models with Scheduled SamplingCode1
A generic diffusion-based approach for 3D human pose prediction in the wildCode1
Retinex Image Enhancement Based on Sequential Decomposition With a Plug-and-Play Framework—0
Denoising Masked AutoEncoders Help Robust ClassificationCode1
Masked Autoencoders for Low dose CT denoising—0
CLIP-Diffusion-LM: Apply Diffusion Model on Image CaptioningCode1
What the DAAM: Interpreting Stable Diffusion Using Cross AttentionCode2
Label-Driven Denoising Framework for Multi-Label Few-Shot Aspect Category DetectionCode1
FP-Diffusion: Improving Score-based Diffusion Models by Enforcing the Underlying Score Fokker-Planck EquationCode1
LW-ISP: A Lightweight Model with ISP and Deep Learning—0
STaSy: Score-based Tabular data SynthesisCode1
Images as Weight Matrices: Sequential Image Generation Through Synaptic Learning RulesCode1
Novel View Synthesis with Diffusion Models—0
On Distillation of Guided Diffusion ModelsCode3
Progressive Text-to-Image Generation—0
clip2latent: Text driven sampling of a pre-trained StyleGAN using denoising diffusion and CLIPCode1
Polar Encoding: A Simple Baseline Approach for Classification with Missing Values—0
Accurate Image Restoration with Attention Retractable TransformerCode1
Clean self-supervised MRI reconstruction from noisy, sub-sampled training data with Robust SSDUCode0
Diffusion Models for Graphs Benefit From Discrete State SpacesCode1
WaveFit: An Iterative and Non-autoregressive Neural Vocoder based on Fixed-Point Iteration—0
EraseNet: A Recurrent Residual Network for Supervised Document Cleaning—0
Improving Sample Quality of Diffusion Models Using Self-Attention GuidanceCode7
OCD: Learning to Overfit with Conditional Diffusion ModelsCode1
Seeing Through the Noisy Dark: Towards Real-world Low-Light Image Enhancement and Denoising—0
Deep Perceptual Enhancement for Medical Image AnalysisCode0
Automatic Summarization for Creative Writing: BART based Pipeline Method for Generating Summary of Movie Scripts—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