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 5851–5900 of 7282 papers

TitleStatusHype
RadBARTsum: Domain Specific Adaption of Denoising Sequence-to-Sequence Models for Abstractive Radiology Report Summarization—0
Wiener filters on graphs and distributed polynomial approximation algorithms—0
Radio Frequency Fingerprint Identification Based on Denoising Autoencoders—0
Radio Frequency Fingerprints Extraction for LTE-V2X: A Channel Estimation Based Methodology—0
Iterative CT Reconstruction via Latent Variable Optimization of Shallow Diffusion Models—0
RAEDiff: Denoising Diffusion Probabilistic Models Based Reversible Adversarial Examples Self-Generation and Self-Recovery—0
RAIN: Real-time Animation of Infinite Video Stream—0
Rakuten’s Participation in WAT 2021: Examining the Effectiveness of Pre-trained Models for Multilingual and Multimodal Machine Translation—0
Unmixing Diffusion for Self-Supervised Hyperspectral Image Denoising—0
Random Conditioning for Diffusion Model Compression with Distillation—0
Random Conditioning with Distillation for Data-Efficient Diffusion Model Compression—0
Unnormalized Variational Bayes—0
Rank-Enhanced Low-Dimensional Convolution Set for Hyperspectral Image Denoising—0
On Probabilistic Embeddings in Optimal Dimension Reduction—0
Rank-one partitioning: formalization, illustrative examples, and a new cluster enhancing strategy—0
A Comprehensive Study of Object Tracking in Low-Light Environments—0
Rao-Blackwell Gradient Estimators for Equivariant Denoising Diffusion—0
Rapid Reconstruction of Extremely Accelerated Liver 4D MRI via Chained Iterative Refinement—0
Unpacking the Individual Components of Diffusion Policy—0
RaSS: Improving Denoising Diffusion Samplers with Reinforced Active Sampling Scheduler—0
aTENNuate: Optimized Real-time Speech Enhancement with Deep SSMs on Raw Audio—0
Raw Waveform-based Speech Enhancement by Fully Convolutional Networks—0
RDSinger: Reference-based Diffusion Network for Singing Voice Synthesis—0
RealDrive: Retrieval-Augmented Driving with Diffusion Models—0
Real Image Denoising With a Locally-Adaptive Bitonic Filter—0
Real Image Restoration via Structure-preserving Complementarity Attention—0
A Comprehensive Approach to Mode Clustering—0
Unpaired image denoising using a generative adversarial network in X-ray CT—0
Real-time and high-throughput Raman signal extraction and processing in CARS hyperspectral imaging—0
Wi-Fi-based Personnel Identity Recognition: Addressing Dataset Imbalance with C-DDPMs—0
Diffusion Models for Wireless Communications—0
Real-time Dynamic MRI Reconstruction using Stacked Denoising Autoencoder—0
Real-time Human Action Recognition Using Locally Aggregated Kinematic-Guided Skeletonlet and Supervised Hashing-by-Analysis Model—0
Unraveling the Temporal Dynamics of the Unet in Diffusion Models—0
Real-Time Radio Technology and Modulation Classification via an LSTM Auto-Encoder—0
Real-time Single-channel Dereverberation and Separation with Time-domainAudio Separation Network—0
Unrolled denoising networks provably learn optimal Bayesian inference—0
Real-Time Video Super-Resolution by Joint Local Inference and Global Parameter Estimation—0
Real-World Denoising via Diffusion Model—0
A Composite Predictive-Generative Approach to Monaural Universal Speech Enhancement—0
A Complex Constrained Total Variation Image Denoising Algorithm with Application to Phase Retrieval—0
Recent progress in image denoising: A training strategy perspective—0
Recognition-Guided Diffusion Model for Scene Text Image Super-Resolution—0
Reconciling "priors" & "priors" without prejudice?—0
Unrolling Nonconvex Graph Total Variation for Image Denoising—0
Reconstruct-and-Generate Diffusion Model for Detail-Preserving Image Denoising—0
Reconstruct Anything Model: a lightweight foundation model for computational imaging—0
Reconstructing the Noise Manifold for Image Denoising—0
Reconstructing the Noise Variance Manifold for Image Denoising—0
Reconstruction of Hidden Representation for Robust Feature Extraction—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