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 4451–4500 of 7282 papers

TitleStatusHype
Inverse Problem of Ultrasound Beamforming with Denoising-Based Regularized SolutionsCode1
Maximum Likelihood Training for Score-Based Diffusion ODEs by High-Order Denoising Score MatchingCode0
CARD: Classification and Regression Diffusion ModelsCode2
Discrete Contrastive Diffusion for Cross-Modal Music and Image GenerationCode1
Defending Observation Attacks in Deep Reinforcement Learning via Detection and DenoisingCode0
Hypernetwork-Based Adaptive Image RestorationCode1
Robust Time Series Denoising with Learnable Wavelet Packet Transform—0
Data-Driven Denoising of Stationary Accelerometer SignalsCode1
A Two-stage Method for Non-extreme Value Salt-and-Pepper Noise Removal—0
gDDIM: Generalized denoising diffusion implicit modelsCode1
Image Generation with Multimodal Priors using Denoising Diffusion Probabilistic Models—0
Self-Supervised Low-Light Image Enhancement Using Discrepant Untrained Network PriorsCode1
Denoising Generalized Expectation-Consistent Approximation for MR Image RecoveryCode0
SAR Despeckling using a Denoising Diffusion Probabilistic ModelCode1
Towards Understanding Graph Neural Networks: An Algorithm Unrolling Perspective—0
AGConv: Adaptive Graph Convolution on 3D Point CloudsCode0
Cross-boosting of WNNM Image Denoising method by Directional Wavelet Packets—0
Robust Deep Ensemble Method for Real-world Image DenoisingCode0
Language-Bridged Spatial-Temporal Interaction for Referring Video Object SegmentationCode1
Neural Diffusion ProcessesCode1
Shape, Light, and Material Decomposition from Images using Monte Carlo Rendering and DenoisingCode2
Fast Unsupervised Brain Anomaly Detection and Segmentation with Diffusion Models—0
Recurrent Video Restoration Transformer with Guided Deformable AttentionCode1
Zero-Shot Voice Conditioning for Denoising Diffusion TTS Models—0
Poisson2Sparse: Self-Supervised Poisson Denoising From a Single ImageCode1
Denoising Fast X-Ray Fluorescence Raster Scans of Paintings—0
LenslessPiCam: A Hardware and Software Platform for Lensless Computational Imaging with a Raspberry PiCode1
Enhanced precision of circadian rhythm by output systemCode0
Modeling sRGB Camera Noise with Normalizing Flows—0
Noise2NoiseFlow: Realistic Camera Noise Modeling without Clean Images—0
CUNI Submission to MT4All Shared Task—0
Far-Field Speaker Recognition Benchmark Derived From The DiPCo Corpus—0
Supervised Denoising of Diffusion-Weighted Magnetic Resonance Images Using a Convolutional Neural Network and Transfer Learning—0
DiVAE: Photorealistic Images Synthesis with Denoising Diffusion Decoder—0
Identifying Arrhythmias Based on ECG Classification Using Enhanced-PCA and Enhanced-SVM Methods—0
Mixed noise reduction via sparse error constraint representation of high frequency image for wildlife imageCode1
Pre-training via Denoising for Molecular Property PredictionCode1
Differentiable programming for functional connectomicsCode0
On Analyzing Generative and Denoising Capabilities of Diffusion-based Deep Generative ModelsCode0
An Effective Fusion Method to Enhance the Robustness of CNN—0
Improved Vector Quantized Diffusion ModelsCode2
A hybrid approach to seismic deblending: when physics meets self-supervision—0
E2S2: Encoding-Enhanced Sequence-to-Sequence Pretraining for Language Understanding and GenerationCode0
Few-Shot Diffusion ModelsCode1
Guided Diffusion Model for Adversarial PurificationCode1
A Continuous Time Framework for Discrete Denoising ModelsCode1
ES-GNN: Generalizing Graph Neural Networks Beyond Homophily with Edge SplittingCode0
Protein Structure and Sequence Generation with Equivariant Denoising Diffusion Probabilistic Models—0
Accelerating Diffusion Models via Early Stop of the Diffusion ProcessCode1
Fine-grained Contrastive Learning for Relation ExtractionCode0
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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