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 1351–1375 of 7282 papers

TitleStatusHype
Ab-initio Contrast Estimation and Denoising of Cryo-EM ImagesCode1
Exploring the Devil in Graph Spectral Domain for 3D Point Cloud AttacksCode1
Open-set Adversarial Defense with Clean-Adversarial Mutual LearningCode1
Riemannian Score-Based Generative ModellingCode1
Content-Noise Complementary Learning for Medical Image DenoisingCode1
Proximal Denoiser for Convergent Plug-and-Play Optimization with Nonconvex RegularizationCode1
Blind ECG Restoration by Operational Cycle-GANsCode1
Unsupervised Denoising of Retinal OCT with Diffusion Probabilistic ModelCode1
Ultra Low-Parameter Denoising: Trainable Bilateral Filter Layers in Computed TomographyCode1
Table Pre-training: A Survey on Model Architectures, Pre-training Objectives, and Downstream TasksCode1
Adaptive Deep PnP Algorithm for Video Snapshot Compressive ImagingCode1
Learning to Denoise Raw Mobile UI Layouts for Improving Datasets at ScaleCode1
Memory-Augmented Deep Conditional Unfolding Network for Pan-SharpeningCode1
Wnet: Audio-Guided Video Object Segmentation via Wavelet-Based Cross-Modal Denoising NetworksCode1
Learning Self-prior for Mesh Denoising using Dual Graph Convolutional NetworksCode1
On Efficient Transformer-Based Image Pre-training for Low-Level VisionCode1
Neuromorphic Camera Denoising using Graph Neural Network-driven TransformersCode1
Sparse Coding with Multi-Layer Decoders using Variance RegularizationCode1
Step-unrolled Denoising Autoencoders for Text GenerationCode1
Formulating Event-based Image Reconstruction as a Linear Inverse Problem with Deep Regularization using Optical FlowCode1
A Conditional Point Diffusion-Refinement Paradigm for 3D Point Cloud CompletionCode1
Label-Efficient Semantic Segmentation with Diffusion ModelsCode1
Embedding Decomposition for Artifacts Removal in EEG SignalsCode1
SAR Image Despeckling Using Continuous Attention ModuleCode1
Learning Robust Recommender from Noisy Implicit FeedbackCode1
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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