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 17011725 of 7282 papers

TitleStatusHype
Deep Speech Synthesis from MRI-Based Articulatory RepresentationsCode1
Deep Multi-Threshold Spiking-UNet for Image ProcessingCode1
ALOcc: Adaptive Lifting-based 3D Semantic Occupancy and Cost Volume-based Flow PredictionCode1
Iterative Gaussianization: from ICA to Random RotationsCode1
Probabilistic Noise2Void: Unsupervised Content-Aware DenoisingCode1
The Tenth NTIRE 2025 Image Denoising Challenge ReportCode1
Adaptive Graph Contrastive Learning for RecommendationCode1
It's Enough: Relaxing Diagonal Constraints in Linear Autoencoders for RecommendationCode1
Joint Demosaicing and Denoising With Self GuidanceCode1
CamoDiffusion: Camouflaged Object Detection via Conditional Diffusion ModelsCode1
An Evaluation of Deep Learning Models for Stock Market Trend PredictionCode1
Deep Universal Blind Image DenoisingCode1
A Bayesian Model of Dose-Response for Cancer Drug StudiesCode1
Joint Graph Rewiring and Feature Denoising via Spectral ResonanceCode1
DEFT: Efficient Fine-Tuning of Diffusion Models by Learning the Generalised h-transformCode1
Joint Low Dose CT Denoising And Kidney SegmentationCode1
Timestep Embedding Tells: It's Time to Cache for Video Diffusion ModelCode1
Joint multi-dimensional dynamic attention and transformer for general image restorationCode1
Memory-Efficient Hierarchical Neural Architecture Search for Image RestorationCode1
KAE: Kolmogorov-Arnold Auto-Encoder for Representation LearningCode1
MIDA: Multiple Imputation using Denoising AutoencodersCode1
JPEG Artifact Correction using Denoising Diffusion Restoration ModelsCode1
Multi-dimensional Visual Prompt Enhanced Image Restoration via Mamba-Transformer AggregationCode1
DeFT-AN: Dense Frequency-Time Attentive Network for Multichannel Speech EnhancementCode1
Noise2Fast: Fast Self-Supervised Single Image Blind DenoisingCode1
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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