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

Showing 42014225 of 7282 papers

TitleStatusHype
A Magnetic Framelet-Based Convolutional Neural Network for Directed Graphs0
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 LIDAR0
Efficient Bi-Level Optimization for Recommendation DenoisingCode0
Denoising Enhanced Distantly Supervised Ultrafine Entity Typing0
BirdSoundsDenoising: Deep Visual Audio Denoising for Bird SoundsCode1
Signal Processing for Implicit Neural Representations0
spatial-dccrn: dccrn equipped with frame-level angle feature and hybrid filtering for multi-channel speech enhancement0
Scale-Agnostic Super-Resolution in MRI using Feature-Based Coordinate Networks0
Gated Recurrent Unit for Video Denoising0
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 Recognition0
Dissipative residual layers for unsupervised implicit parameterization of data manifolds0
LION: Latent Point Diffusion Models for 3D Shape GenerationCode2
Common Corruption Robustness of Point Cloud Detectors: Benchmark and Enhancement0
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
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1SINDyPSNR81Unverified
2Pixel-shuffling DownsamplingPSNR38.4Unverified
3TWSCPSNR37.93Unverified
4CBDNet(Syn)PSNR37.57Unverified
5MCWNNMPSNR37.38Unverified
6Han et alPSNR35.95Unverified
7FFDNetPSNR34.4Unverified
8TNRDPSNR33.65Unverified
9CDnCNN-BPSNR32.43Unverified
10NLRNPSNR30.8Unverified
#ModelMetricClaimedVerifiedStatus
1DRUnet_Poisson_0.01Average PSNR (dB)33.92Unverified
#ModelMetricClaimedVerifiedStatus
1DRANetAverage PSNR39.64Unverified
#ModelMetricClaimedVerifiedStatus
1PCNN+RL+HMEAverage84.61Unverified