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 1376–1400 of 7282 papers

TitleStatusHype
DeeDSR: Towards Real-World Image Super-Resolution via Degradation-Aware Stable DiffusionCode1
Dual Residual Attention Network for Image DenoisingCode1
Dual Prior Unfolding for Snapshot Compressive ImagingCode1
DVIS++: Improved Decoupled Framework for Universal Video SegmentationCode1
DU-GAN: Generative Adversarial Networks with Dual-Domain U-Net Based Discriminators for Low-Dose CT DenoisingCode1
Multi-View Learning with Context-Guided Receptance for Image DenoisingCode1
Multi-view Self-supervised Disentanglement for General Image DenoisingCode1
Score-based denoising for atomic structure identificationCode1
Interpreting and Improving Diffusion Models from an Optimization PerspectiveCode1
CPT: A Pre-Trained Unbalanced Transformer for Both Chinese Language Understanding and GenerationCode1
CrackSegDiff: Diffusion Probability Model-based Multi-modal Crack SegmentationCode1
NAP: Neural 3D Articulation PriorCode1
NBNet: Noise Basis Learning for Image Denoising with Subspace ProjectionCode1
Chip Placement with Diffusion ModelsCode1
Decoupled Data Consistency with Diffusion Purification for Image RestorationCode1
Edge-preserving noise for diffusion modelsCode1
EC-Conf: An Ultra-fast Diffusion Model for Molecular Conformation Generation with Equivariant ConsistencyCode1
ACLM: A Selective-Denoising based Generative Data Augmentation Approach for Low-Resource Complex NERCode1
Simultaneous Image-to-Zero and Zero-to-Noise: Diffusion Models with Analytical Image AttenuationCode1
EDformer: Transformer-Based Event Denoising Across Varied Noise LevelsCode1
EDCNN: Edge enhancement-based Densely Connected Network with Compound Loss for Low-Dose CT DenoisingCode1
Neural Thermodynamic Integration: Free Energies from Energy-based Diffusion ModelsCode1
Neural Transfer Learning for Repairing Security Vulnerabilities in C CodeCode1
Inverse Problem of Ultrasound Beamforming with Denoising-Based Regularized SolutionsCode1
Decoder Denoising Pretraining for Semantic SegmentationCode1
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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