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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 6376–6400 of 7282 papers

TitleStatusHype
Classifier and Exemplar Synthesis for Zero-Shot LearningCode0
From neural PCA to deep unsupervised learningCode0
Labeling, Cutting, Grouping: an Efficient Text Line Segmentation Method for Medieval ManuscriptsCode0
From Patches to Images: A Nonparametric Generative ModelCode0
Seismic Random Noise Attenuation Based on Non-IID Pixel-Wise Gaussian Noise ModelingCode0
A Semisupervised Approach for Language Identification based on Ladder NetworksCode0
You Only Look One Step: Accelerating Backpropagation in Diffusion Sampling with Gradient ShortcutsCode0
From Shortcuts to Triggers: Backdoor Defense with Denoised PoECode0
Designing Stable Neural Networks using Convex Analysis and ODEsCode0
Resurrecting Label Propagation for Graphs with Heterophily and Label NoiseCode0
Despeckling Sentinel-1 GRD images by deep learning and application to narrow river segmentationCode0
From Text to Mask: Localizing Entities Using the Attention of Text-to-Image Diffusion ModelsCode0
DestripeCycleGAN: Stripe Simulation CycleGAN for Unsupervised Infrared Image DestripingCode0
Classifier-Free Guidance inside the Attraction Basin May Cause MemorizationCode0
Single Stage Adaptive Multi-Attention Network for Image RestorationCode0
Using Autoencoders on Differentially Private Federated Learning GANsCode0
Zero-shot denoising via neural compression: Theoretical and algorithmic frameworkCode0
Detect and Defense Against Adversarial Examples in Deep Learning using Natural Scene Statistics and Adaptive DenoisingCode0
Language-Aware Multilingual Machine Translation with Self-Supervised LearningCode0
Detecting Patch Adversarial Attacks with Image ResidualsCode0
Fully Adaptive Time-Varying Wave-Shape Model: Applications in Biomedical Signal ProcessingCode0
Multi-Object Self-Supervised Depth DenoisingCode0
Fully Convolutional Network with Multi-Step Reinforcement Learning for Image ProcessingCode0
Binding via Reconstruction ClusteringCode0
Detection of manatee vocalisations using the Audio Spectrogram TransformerCode0
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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