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 16761700 of 7282 papers

TitleStatusHype
Can Language Models Perform Robust Reasoning in Chain-of-thought Prompting with Noisy Rationales?Code1
DEFT: Efficient Fine-Tuning of Diffusion Models by Learning the Generalised h-transformCode1
MatchDiffusion: Training-free Generation of Match-cutsCode1
MCDDPM: Multichannel Conditional Denoising Diffusion Model for Unsupervised Anomaly Detection in Brain MRICode1
Diff-UNet: A Diffusion Embedded Network for Volumetric SegmentationCode1
BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and ComprehensionCode1
Masked Pre-training Enables Universal Zero-shot DenoiserCode1
Deep Random Projector: Accelerated Deep Image PriorCode1
Deep Recurrent Neural Networks for ECG Signal DenoisingCode1
Intelligent Painter: Picture Composition With Resampling Diffusion ModelCode1
Deep Reparametrization of Multi-Frame Super-Resolution and DenoisingCode1
Deep Universal Blind Image DenoisingCode1
Deep Residual Learning for Channel Estimation in Intelligent Reflecting Surface-Assisted Multi-User CommunicationsCode1
Deep Residual Network Empowered Channel Estimation for IRS-Assisted Multi-User Communication SystemsCode1
Denoising Diffusion Autoencoders are Unified Self-supervised LearnersCode1
Mask prior-guided denoising diffusion improves inverse protein foldingCode1
TASER: Temporal Adaptive Sampling for Fast and Accurate Dynamic Graph Representation LearningCode1
Deep Semantic Statistics Matching (D2SM) Denoising NetworkCode1
Interpolating between Images with Diffusion ModelsCode1
Probabilistic Noise2Void: Unsupervised Content-Aware DenoisingCode1
Inverse Problem of Ultrasound Beamforming with Denoising-Based Regularized SolutionsCode1
CamoDiffusion: Camouflaged Object Detection via Conditional Diffusion ModelsCode1
ISCL: Interdependent Self-Cooperative Learning for Unpaired Image DenoisingCode1
Deep Multi-Threshold Spiking-UNet for Image ProcessingCode1
An Evaluation of Deep Learning Models for Stock Market Trend PredictionCode1
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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