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 4351–4375 of 7282 papers

TitleStatusHype
On tuning consistent annealed sampling for denoising score matching—0
Exploring Self-Attention Mechanisms for Speech Separation—0
Two-stage Deep Denoising with Self-guided Noise Attention for Multimodal Medical Images—0
AdaIN-Switchable CycleGAN for Efficient Unsupervised Low-Dose CT Denoising—0
Two-stage Denoising Diffusion Model for Source Localization in Graph Inverse Problems—0
Two-Stage Learning for Uplink Channel Estimation in One-Bit Massive MIMO—0
Two-Stage Monte Carlo Denoising with Adaptive Sampling and Kernel Pool—0
Operational vs Convolutional Neural Networks for Image Denoising—0
Ophiuchus: Scalable Modeling of Protein Structures through Hierarchical Coarse-graining SO(3)-Equivariant Autoencoders—0
Optical Diffusion Models for Image Generation—0
Optical Fiber Fault Detection and Localization in a Noisy OTDR Trace Based on Denoising Convolutional Autoencoder and Bidirectional Long Short-Term Memory—0
Optical Fringe Patterns Filtering Based on Multi-Stage Convolution Neural Network—0
Multi-Sensor Diffusion-Driven Optical Image Translation for Large-Scale Applications—0
Optimal Combination of Image Denoisers—0
Optimal Denoising in Score-Based Generative Models: The Role of Data Regularity—0
AdaDiffSR: Adaptive Region-aware Dynamic Acceleration Diffusion Model for Real-World Image Super-Resolution—0
Optimal Estimation of Shared Singular Subspaces across Multiple Noisy Matrices—0
Optimally Stabilized PET Image Denoising Using Trilateral Filtering—0
POS: A Prompts Optimization Suite for Augmenting Text-to-Video Generation—0
Optimal Rates of Statistical Seriation—0
Optimal Receive Beamforming for Over-the-Air Computation—0
AdaDiff: Adaptive Step Selection for Fast Diffusion Models—0
Optimal training of finitely-sampled quantum reservoir computers for forecasting of chaotic dynamics—0
Two-Stage Pretraining for Molecular Property Prediction in the Wild—0
Ada3Diff: Defending against 3D Adversarial Point Clouds via Adaptive Diffusion—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