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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 4826–4850 of 7282 papers

TitleStatusHype
Noise2SR: Learning to Denoise from Super-Resolved Single Noisy Fluorescence Image—0
vec2text with Round-Trip Translations—0
Considering Image Information and Self-similarity: A Compositional Denoising NetworkCode0
Robust Product Classification with Instance-Dependent Noise—0
PET image denoising based on denoising diffusion probabilistic models—0
Blurring Diffusion Models—0
Soft Diffusion: Score Matching for General Corruptions—0
Manifold Rewiring for Unlabeled Imaging—0
G2L: A Global to Local Alignment Method for Unsupervised Domain Adaptive Semantic Segmentation—0
Learning to Predict on Octree for Scalable Point Cloud Geometry Coding—0
LRT: An Efficient Low-Light Restoration Transformer for Dark Light Field Images—0
Video Restoration with a Deep Plug-and-Play Prior—0
A Multi-scale Video Denoising Algorithm for Raw Image—0
Large Graph Signal Denoising with Application to Differential PrivacyCode0
Geometric and Learning-based Mesh Denoising: A Comprehensive Survey—0
PCDNF: Revisiting Learning-based Point Cloud Denoising via Joint Normal Filtering—0
Practical Operator Sketching Framework for Accelerating Iterative Data-Driven Solutions in Inverse Problems—0
ELMformer: Efficient Raw Image Restoration with a Locally Multiplicative TransformerCode0
Denoising Architecture for Unsupervised Anomaly Detection in Time-SeriesCode0
SSORN: Self-Supervised Outlier Removal Network for Robust Homography Estimation—0
Representation Learning based and Interpretable Reactor System Diagnosis Using Denoising Padded Autoencoder—0
Stabilize, Decompose, and Denoise: Self-Supervised Fluoroscopy Denoising—0
Towards Adversarial Purification using Denoising AutoEncoders—0
Detection and Classification of Brain tumors Using Deep Convolutional Neural Networks—0
Source Camera Identification with Multi-Scale Feature Fusion NetworkCode0
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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