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 1176–1200 of 7282 papers

TitleStatusHype
DeSTSeg: Segmentation Guided Denoising Student-Teacher for Anomaly DetectionCode1
An Unsupervised Deep Learning Approach for Real-World Image DenoisingCode1
DermoSegDiff: A Boundary-aware Segmentation Diffusion Model for Skin Lesion DelineationCode1
Diffusion Policies creating a Trust Region for Offline Reinforcement LearningCode1
Diffusion Posterior Illumination for Ambiguity-aware Inverse RenderingCode1
A Flexible Framework for Designing Trainable Priors with Adaptive Smoothing and Game EncodingCode1
DETA: Denoised Task Adaptation for Few-Shot LearningCode1
CLEARER: Multi-Scale Neural Architecture Search for Image RestorationCode1
Diffusion Probabilistic Priors for Zero-Shot Low-Dose CT Image DenoisingCode1
CleanUMamba: A Compact Mamba Network for Speech Denoising using Channel PruningCode1
Anomaly Detection using Score-based Perturbation ResilienceCode1
AdvAD: Exploring Non-Parametric Diffusion for Imperceptible Adversarial AttacksCode1
DenoMamba: A fused state-space model for low-dose CT denoisingCode1
AP-BSN: Self-Supervised Denoising for Real-World Images via Asymmetric PD and Blind-Spot NetworkCode1
Diffusion with Forward Models: Solving Stochastic Inverse Problems Without Direct SupervisionCode1
Denoising Relation Extraction from Document-level Distant SupervisionCode1
Denoising Point Clouds in Latent Space via Graph Convolution and Invertible Neural NetworkCode1
A Physics-based Noise Formation Model for Extreme Low-light Raw DenoisingCode1
Digging into contrastive learning for robust depth estimation with diffusion modelsCode1
Denoising Task Routing for Diffusion ModelsCode1
Denoising Normalizing FlowCode1
Denoising Multi-Source Weak Supervision for Neural Text ClassificationCode1
Composer Style-specific Symbolic Music Generation Using Vector Quantized Discrete Diffusion ModelsCode1
Denoising of 3D MR images using a voxel-wise hybrid residual MLP-CNN model to improve small lesion diagnostic confidenceCode1
DenoSent: A Denoising Objective for Self-Supervised Sentence Representation LearningCode1
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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