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 5651–5700 of 7282 papers

TitleStatusHype
Undecimated Wavelet Transform for Word Embedded Semantic Marginal Autoencoder in Security improvement and Denoising different Languages—0
Least Squares Regression Can Exhibit Under-Parameterized Double Descent—0
Plug-And-Play Learned Gaussian-mixture Approximate Message Passing—0
Plug and play methods for magnetic resonance imaging (long version)—0
Plug-and-Play Posterior Sampling for Blind Inverse Problems—0
Plug-and-Play Priors for Bright Field Electron Tomography and Sparse Interpolation—0
Under-Sampled High-Dimensional Data Recovery via Symbiotic Multi-Prior Tensor Reconstruction—0
Plug-and-Play Unplugged: Optimization Free Reconstruction using Consensus Equilibrium—0
Pluralistic Aging Diffusion Autoencoder—0
Understanding Representation Dynamics of Diffusion Models via Low-Dimensional Modeling—0
Understanding Symmetric Smoothing Filters: A Gaussian Mixture Model Perspective—0
PNEN: Pyramid Non-Local Enhanced Networks—0
Unfolded proximal neural networks for robust image Gaussian denoising—0
A CT Image Denoising Method Based on Projection Domain Feature—0
Underwater Acoustic Signal Denoising Algorithms: A Survey of the State-of-the-art—0
Point Cloud Denoising and Outlier Detection with Local Geometric Structure by Dynamic Graph CNN—0
Point cloud denoising based on tensor Tucker decomposition—0
Underwater Color Restoration Using U-Net Denoising Autoencoder—0
Point Cloud Denoising via Moving RPCA: MRPCA—0
Point Cloud Denoising With Fine-Granularity Dynamic Graph Convolutional Networks—0
Point Cloud Resampling with Learnable Heat Diffusion—0
Denoising and Verification Cross-Layer Ensemble Against Black-box Adversarial Attacks—0
PointDiffuse: A Dual-Conditional Diffusion Model for Enhanced Point Cloud Semantic Segmentation—0
Point Processes and spatial statistics in time-frequency analysis—0
Point Spread Function Modelling for Wide Field Small Aperture Telescopes with a Denoising Autoencoder—0
Pointwise shape-adaptive DCT for high-quality deblocking of compressed color images—0
Poisoning MorphNet for Clean-Label Backdoor Attack to Point Clouds—0
Underwater Image Enhancement Using Pre-trained Transformer—0
PFCM: Poisson flow consistency models for low-dose CT image denoising—0
Poisson Image Deconvolution by a Plug-and-Play Quantum Denoising Scheme—0
Poisson Image Denoising Using Best Linear Prediction: A Post-processing Framework—0
Poisson Inverse Problems by the Plug-and-Play scheme—0
Poisson Noise Reduction with Higher-order Natural Image Prior Model—0
Poisson noise reduction with non-local PCA—0
Polarimetric Normal Stereo—0
Polarized Color Image Denoising—0
Polarized Color Image Denoising using Pocoformer—0
PolMERLIN: Self-Supervised Polarimetric Complex SAR Image Despeckling with Masked Networks—0
PolyDiff: Generating 3D Polygonal Meshes with Diffusion Models—0
Positive2Negative: Breaking the Information-Lossy Barrier in Self-Supervised Single Image Denoising—0
PostCast: Generalizable Postprocessing for Precipitation Nowcasting via Unsupervised Blurriness Modeling—0
Likelihood Annealing: Fast Calibrated Uncertainty for Regression—0
Posterior-Mean Denoising Diffusion Model for Realistic PET Image Reconstruction—0
Posterior Sampling with Denoising Oracles via Tilted Transport—0
Posterior Temperature Optimization in Variational Inference for Inverse Problems—0
Postprocessing of Compressed Images via Sequential Denoising—0
Post-Training Quantization for Diffusion Transformer via Hierarchical Timestep Grouping—0
Powerful Lossy Compression for Noisy Images—0
Powers of layers for image-to-image translation—0
A Cross Validation Framework for Signal Denoising with Applications to Trend Filtering, Dyadic CART and Beyond—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