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 4451–4475 of 7282 papers

TitleStatusHype
Patient representation learning and interpretable evaluation using clinical notes—0
Pattern Denoising in Molecular Associative Memory using Pairwise Markov Random Field Models—0
PatternPaint: Practical Layout Pattern Generation Using Diffusion-Based Inpainting—0
Active Mean Fields for Probabilistic Image Segmentation: Connections with Chan-Vese and Rudin-Osher-Fatemi Models—0
PC2: Projection-Conditioned Point Cloud Diffusion for Single-Image 3D Reconstruction—0
PCB Defect Detection Using Denoising Convolutional Autoencoders—0
PCDNF: Revisiting Learning-based Point Cloud Denoising via Joint Normal Filtering—0
PCGAN-CHAR: Progressively Trained Classifier Generative Adversarial Networks for Classification of Noisy Handwritten Bangla Characters—0
PCM : Picard Consistency Model for Fast Parallel Sampling of Diffusion Models—0
PCRDiffusion: Diffusion Probabilistic Models for Point Cloud Registration—0
PDFactor: Learning Tri-Perspective View Policy Diffusion Field for Multi-Task Robotic Manipulation—0
Ultrasound Speckle Suppression and Denoising using MRI-derived Normalizing Flow Priors—0
UltraVSR: Achieving Ultra-Realistic Video Super-Resolution with Efficient One-Step Diffusion Space—0
Unboxed: Geometrically and Temporally Consistent Video Outpainting—0
Perceptual-based deep-learning denoiser as a defense against adversarial attacks on ASR systems—0
Perceptual Kalman Filters: Online State Estimation under a Perfect Perceptual-Quality Constraint—0
Perceptually Optimized Generative Adversarial Network for Single Image Dehazing—0
AeGAN: Time-Frequency Speech Denoising via Generative Adversarial Networks—0
Performance Analysis of Plug-and-Play ADMM: A Graph Signal Processing Perspective—0
Performance Analysis of Spatial and Transform Filters for Efficient Image Noise Reduction—0
Performance characterization of a novel deep learning-based MR image reconstruction pipeline—0
Performance Limits of Stochastic Sub-Gradient Learning, Part I: Single Agent Case—0
PeriodGrad: Towards Pitch-Controllable Neural Vocoder Based on a Diffusion Probabilistic Model—0
Permuted and Unlinked Monotone Regression in R^d: an approach based on mixture modeling and optimal transport—0
Personalize Anything for Free with Diffusion Transformer—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