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 6101–6150 of 7282 papers

TitleStatusHype
Robust Time Series Denoising with Learnable Wavelet Packet Transform—0
Robust Training of Neural Networks at Arbitrary Precision and Sparsity—0
Robust Transfer Principal Component Analysis with Rank Constraints—0
Robust Unsupervised Neural Machine Translation with Adversarial Denoising Training—0
Unsupervised Multi-Criteria Adversarial Detection in Deep Image Retrieval—0
ROG_PL: Robust Open-Set Graph Learning via Region-Based Prototype Learning—0
Rogue Emitter Detection Using Hybrid Network of Denoising Autoencoder and Deep Metric Learning—0
Accelerating Mobile Edge Generation (MEG) by Constrained Learning—0
ROIC-DM: Robust Text Inference and Classification via Diffusion Model—0
Rolling Diffusion Models—0
ROODI: Reconstructing Occluded Objects with Denoising Inpainters—0
RoPECraft: Training-Free Motion Transfer with Trajectory-Guided RoPE Optimization on Diffusion Transformers—0
Accelerating Linear Recurrent Neural Networks for the Edge with Unstructured Sparsity—0
Unsupervised Neural Machine Translation for English and Manipuri—0
Roto-Translation Invariant Metrics on Position-Orientation Space—0
RAR-U-Net: a Residual Encoder to Attention Decoder by Residual Connections Framework for Spine Segmentation under Noisy Labels—0
RSEND: Retinex-based Squeeze and Excitation Network with Dark Region Detection for Efficient Low Light Image Enhancement—0
RSR-NF: Neural Field Regularization by Static Restoration Priors for Dynamic Imaging—0
RuleAgent: Discovering Rules for Recommendation Denoising with Autonomous Language Agents—0
Rule of Three for Superresolution of Still Images with Applications to Compression and Denoising—0
WTEFNet: Real-Time Low-Light Object Detection for Advanced Driver-Assistance Systems—0
Run-Time Adaptation of Neural Beamforming for Robust Speech Dereverberation and Denoising—0
RViDeformer: Efficient Raw Video Denoising Transformer with a Larger Benchmark Dataset—0
S^2AG-Vid: Enhancing Multi-Motion Alignment in Video Diffusion Models via Spatial and Syntactic Attention-Based Guidance—0
X-Adapter: Adding Universal Compatibility of Plugins for Upgraded Diffusion Model—0
S2GO: Streaming Sparse Gaussian Occupancy Prediction—0
S2S-WTV: Seismic Data Noise Attenuation Using Weighted Total Variation Regularized Self-Supervised Learning—0
S5 Framework: A Review of Self-Supervised Shared Semantic Space Optimization for Multimodal Zero-Shot Learning—0
Unsupervised patient representations from clinical notes with interpretable classification decisions—0
Saddlepoints in Unsupervised Least Squares—0
Accelerating Image Generation with Sub-path Linear Approximation Model—0
SafeDiffuser: Safe Planning with Diffusion Probabilistic Models—0
Safety-Critical Traffic Simulation with Guided Latent Diffusion Model—0
Accelerating GMM-based patch priors for image restoration: Three ingredients for a 100 speed-up—0
SAIL: Search-Augmented Instruction Learning—0
Accelerating Diffusion Sampling via Exploiting Local Transition Coherence—0
Salient Conditional Diffusion for Defending Against Backdoor Attacks—0
Salt and pepper noise removal method based on stationary Framelet transform with non-convex sparsity regularization—0
Only Relevant Information Matters: Filtering Out Noisy Samples to Boost RL—0
Samples Are Useful? Not Always: denoising policy gradient updates using variance explained—0
Sampling Based Scene-Space Video Processing—0
Sampling, Diffusions, and Stochastic Localization—0
Sampling is as easy as learning the score: theory for diffusion models with minimal data assumptions—0
Generative diffusion for perceptron problems: statistical physics analysis and efficient algorithms—0
Sapinet: A sparse event-based spatiotemporal oscillator for learning in the wild—0
SAR2EO: A High-resolution Image Translation Framework with Denoising Enhancement—0
Unsupervised PET Reconstruction from a Bayesian Perspective—0
SAR Despeckling via Regional Denoising Diffusion Probabilistic Model—0
SAR Image Despeckling Based on Convolutional Denoising Autoencoder—0
SAR Image Synthesis with Diffusion Models—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