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 4601–4625 of 7282 papers

TitleStatusHype
Whiteness-based bilevel estimation of weighted TV parameter maps for image denoising—0
Pre-Training with Diffusion models for Dental Radiography segmentation—0
Pre-training with Fractional Denoising to Enhance Molecular Property Prediction—0
Price of Stability in Quality-Aware Federated Learning—0
Unified Convergence Analysis for Score-Based Diffusion Models with Deterministic Samplers—0
Principal Basis Analysis in Sparse Representation—0
PriorDiffusion: Leverage Language Prior in Diffusion Models for Monocular Depth Estimation—0
A Critical Analysis of Patch Similarity Based Image Denoising Algorithms—0
PriorGrad: Improving Conditional Denoising Diffusion Models with Data-Dependent Adaptive Prior—0
Prioritize Denoising Steps on Diffusion Model Preference Alignment via Explicit Denoised Distribution Estimation—0
Prior Knowledge Input to Improve LSTM Auto-encoder-based Characterization of Vehicular Sensing Data—0
PRISM: Progressive Restoration for Scene Graph-based Image Manipulation—0
Unified Directly Denoising for Both Variance Preserving and Variance Exploding Diffusion Models—0
Privacy-Preserving Encrypted Low-Dose CT Denoising—0
PRNU Emphasis: a Generalization of the Multiplicative Model—0
Proactively Predicting Dynamic 6G Link Blockages Using LiDAR and In-Band Signatures—0
Learning Robust Recommenders through Cross-Model Agreement—0
Unified framework for diffusion generative models in SO(3): applications in computer vision and astrophysics—0
Unified Geometry and Color Compression Framework for Point Clouds via Generative Diffusion Priors—0
Probabilistic Inference of Binary Markov Random Fields in Spiking Neural Networks through Mean-field Approximation—0
Probabilistic Prior Driven Attention Mechanism Based on Diffusion Model for Imaging Through Atmospheric Turbulence—0
Probabilistic Selective Encryption of Convolutional Neural Networks for Hierarchical Services—0
Probabilistic self-learning framework for Low-dose CT Denoising—0
Probabilities-Informed Machine Learning—0
Procedural Kernel Networks—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