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 951–975 of 7282 papers

TitleStatusHype
Adversarial purification with Score-based generative modelsCode1
DPMesh: Exploiting Diffusion Prior for Occluded Human Mesh RecoveryCode1
Crystal Structure Prediction by Joint Equivariant DiffusionCode1
Cross-Level Distillation and Feature Denoising for Cross-Domain Few-Shot ClassificationCode1
A Continuous Time Framework for Discrete Denoising ModelsCode1
Curriculum Disentangled Recommendation with Noisy Multi-feedbackCode1
GroupCDL: Interpretable Denoising and Compressed Sensing MRI via Learned Group-Sparsity and Circulant AttentionCode1
Image Restoration via Frequency SelectionCode1
From Denoising Training to Test-Time Adaptation: Enhancing Domain Generalization for Medical Image SegmentationCode1
From Denoising to Compressed SensingCode1
DS-Fusion: Artistic Typography via Discriminated and Stylized DiffusionCode1
DT-LSD: Deformable Transformer-based Line Segment DetectionCode1
From Rank Estimation to Rank Approximation: Rank Residual Constraint for Image RestorationCode1
Dual convolutional neural network with attention for image blind denoisingCode1
COVE: Unleashing the Diffusion Feature Correspondence for Consistent Video EditingCode1
Dual-Diffusion: Dual Conditional Denoising Diffusion Probabilistic Models for Blind Super-Resolution Reconstruction in RSIsCode1
Dual Prior Unfolding for Snapshot Compressive ImagingCode1
Dual Residual Attention Network for Image DenoisingCode1
CoT-BERT: Enhancing Unsupervised Sentence Representation through Chain-of-ThoughtCode1
DU-GAN: Generative Adversarial Networks with Dual-Domain U-Net Based Discriminators for Low-Dose CT DenoisingCode1
Boundary-aware Contrastive Learning for Semi-supervised Nuclei Instance SegmentationCode1
ADDP: Learning General Representations for Image Recognition and Generation with Alternating Denoising Diffusion ProcessCode1
Boundary Guided Learning-Free Semantic Control with Diffusion ModelsCode1
Let's Rectify Step by Step: Improving Aspect-based Sentiment Analysis with Diffusion ModelsCode1
From Denoising Diffusions to Denoising Markov ModelsCode1
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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