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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 2901–2925 of 7282 papers

TitleStatusHype
Towards High-quality HDR Deghosting with Conditional Diffusion Models—0
TRIALSCOPE: A Unifying Causal Framework for Scaling Real-World Evidence Generation with Biomedical Language Models—0
Non-Autoregressive Diffusion-based Temporal Point Processes for Continuous-Time Long-Term Event Prediction—0
Generating HSR Bogie Vibration Signals via Pulse Voltage-Guided Conditional Diffusion ModelCode1
LatentWarp: Consistent Diffusion Latents for Zero-Shot Video-to-Video Translation—0
Robust Graph Clustering via Meta Weighting for Noisy GraphsCode0
Generate What You Prefer: Reshaping Sequential Recommendation via Guided DiffusionCode1
Brain-like Flexible Visual Inference by Harnessing Feedback-Feedforward AlignmentCode0
The Missing U for Efficient Diffusion Models—0
From Denoising Training to Test-Time Adaptation: Enhancing Domain Generalization for Medical Image SegmentationCode1
Medical Image Denosing via Explainable AI Feature Preserving Loss—0
Diffusion Reconstruction of Ultrasound Images with Informative Uncertainty—0
DiffusionVID: Denoising Object Boxes with Spatio-temporal Conditioning for Video Object DetectionCode1
Conditional Denoising Diffusion Probabilistic Models for Data Reconstruction Enhancement in Wireless Communications—0
Generative Neural Fields by Mixtures of Neural Implicit Functions—0
D4Explainer: In-Distribution GNN Explanations via Discrete Denoising DiffusionCode1
Asymmetric Diffusion Based Channel-Adaptive Secure Wireless Semantic Communications—0
A Scalable Training Strategy for Blind Multi-Distribution Noise Removal—0
DPATD: Dual-Phase Audio Transformer for Denoising—0
DiffSpectralNet : Unveiling the Potential of Diffusion Models for Hyperspectral Image Classification—0
INCODE: Implicit Neural Conditioning with Prior Knowledge EmbeddingsCode1
Customizing 360-Degree Panoramas through Text-to-Image Diffusion ModelsCode1
Successfully Applying Lottery Ticket Hypothesis to Diffusion ModelCode0
MCRAGE: Synthetic Healthcare Data for FairnessCode0
Style Description based Text-to-Speech with Conditional Prosodic Layer Normalization based Diffusion GAN—0
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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