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 1301–1325 of 7282 papers

TitleStatusHype
Contrastive Denoising Score for Text-guided Latent Diffusion Image EditingCode1
Generative diffusion model with inverse renormalization group flowsCode1
A CNN-Based Blind Denoising Method for Endoscopic ImagesCode1
CT-Mamba: A Hybrid Convolutional State Space Model for Low-Dose CT DenoisingCode1
Empowering Diffusion Models on the Embedding Space for Text GenerationCode1
CTformer: Convolution-free Token2Token Dilated Vision Transformer for Low-dose CT DenoisingCode1
DiffSim: Taming Diffusion Models for Evaluating Visual SimilarityCode1
Diffusion-based Pose Refinement and Muti-hypothesis Generation for 3D Human Pose EstimaitonCode1
DiffusionVID: Denoising Object Boxes with Spatio-temporal Conditioning for Video Object DetectionCode1
DiffCharge: Generating EV Charging Scenarios via a Denoising Diffusion ModelCode1
DiffCMR: Fast Cardiac MRI Reconstruction with Diffusion Probabilistic ModelsCode1
Curriculum Disentangled Recommendation with Noisy Multi-feedbackCode1
Goal-Conditioned Imitation Learning using Score-based Diffusion PoliciesCode1
DiffAR: Denoising Diffusion Autoregressive Model for Raw Speech Waveform GenerationCode1
4DenoiseNet: Adverse Weather Denoising from Adjacent Point CloudsCode1
Graph Collaborative Signals Denoising and Augmentation for RecommendationCode1
Graph Information Bottleneck for Subgraph RecognitionCode1
AdaGNN: Graph Neural Networks with Adaptive Frequency Response FilterCode1
Score-based denoising for atomic structure identificationCode1
G-SimCLR : Self-Supervised Contrastive Learning with Guided Projection via Pseudo LabellingCode1
DiffDA: a Diffusion Model for Weather-scale Data AssimilationCode1
A cross Transformer for image denoisingCode1
Guided Diffusion Sampling on Function Spaces with Applications to PDEsCode1
Convergence Guarantees for Non-Convex Optimisation with Cauchy-Based PenaltiesCode1
Chip Placement with Diffusion 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