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 4526–4550 of 7282 papers

TitleStatusHype
Self-Supervised Pre-Training for Deep Image Prior-Based Robust PET Image Denoising—0
Supervised topological data analysis for MALDI mass spectrometry imaging applications—0
Denoising Diffusion Samplers—0
Spatial-temporal Transformer-guided Diffusion based Data Augmentation for Efficient Skeleton-based Action Recognition—0
Temporal Segment Transformer for Action Segmentation—0
CUR Transformer: A Convolutional Unbiased Regional Transformer for Image DenoisingCode0
Implicit neural representations for unsupervised super-resolution and denoising of 4D flow MRI—0
Denoising diffusion algorithm for inverse design of microstructures with fine-tuned nonlinear material properties—0
Practical Knowledge Distillation: Using DNNs to Beat DNNs—0
PIFON-EPT: MR-Based Electrical Property Tomography Using Physics-Informed Fourier Networks—0
Controlled and Conditional Text to Image Generation with Diffusion Prior—0
Likelihood Annealing: Fast Calibrated Uncertainty for Regression—0
Learning Gradually Non-convex Image Priors Using Score Matching—0
Spatio-Temporal Denoising Graph Autoencoders with Data Augmentation for Photovoltaic Timeseries Data Imputation—0
I2V: Towards Texture-Aware Self-Supervised Blind Denoising using Self-Residual Learning for Real-World Images—0
Restoration based Generative ModelsCode0
An Efficient and Robust Method for Chest X-Ray Rib Suppression that Improves Pulmonary Abnormality Diagnosis—0
One-Pot Multi-Frame Denoising—0
Bag of Tricks for Effective Language Model Pretraining and Downstream Adaptation: A Case Study on GLUE—0
When Visible-to-Thermal Facial GAN Beats Conditional Diffusion—0
Low Latency Video Denoising for Online Conferencing Using CNN Architectures—0
AliasNet: Alias Artefact Suppression Network for Accelerated Phase-Encode MRI—0
Adversarial Contrastive Distillation with Adaptive Denoising—0
DeepSpace: Dynamic Spatial and Source Cue Based Source Separation for Dialog Enhancement—0
Explicit Diffusion of Gaussian Mixture Model Based Image Priors—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