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 6976–7000 of 7282 papers

TitleStatusHype
Image denoising using complex-valued deep CNNCode0
Deep Learning of Radiative Atmospheric Transfer with an AutoencoderCode0
Image denoising using deep CNN with batch renormalizationCode0
Manifold Denoising by Nonlinear Robust Principal Component AnalysisCode0
Textless Speech-to-Speech Translation With Limited Parallel DataCode0
Manifold Modeling in Embedded Space: A Perspective for Interpreting Deep Image PriorCode0
Combatting Adversarial Attacks through Denoising and Dimensionality Reduction: A Cascaded Autoencoder ApproachCode0
On Designing Diffusion Autoencoders for Efficient Generation and Representation LearningCode0
Pseudo-Siamese Blind-Spot Transformers for Self-Supervised Real-World DenoisingCode0
Adaptive Bayesian Multivariate Spline Knot Inference with Prior Specifications on Model ComplexityCode0
Eigen-CNN: Eigenimages Plus Eigennoise Level Maps Guided Network for Hyperspectral Image DenoisingCode0
Deep Neural Networks Motivated by Partial Differential EquationsCode0
Adaptive aggregation of Monte Carlo augmented decomposed filters for efficient group-equivariant convolutional neural networkCode0
Burst Denoising with Kernel Prediction NetworksCode0
Electro-Magnetic Side-Channel Attack Through Learned Denoising and ClassificationCode0
Anomaly Detection and Prototype Selection Using Polyhedron CurvatureCode0
Contrastive Conditional Latent Diffusion for Audio-visual SegmentationCode0
Towards Deep Neural Network Architectures Robust to Adversarial ExamplesCode0
A data driven approach to classify descriptors based on their efficiency in translating noisy trajectories into physically-relevant informationCode0
ELMformer: Efficient Raw Image Restoration with a Locally Multiplicative TransformerCode0
Image Denoising with Control over Deep Network HallucinationCode0
MaskBlur: Spatial and Angular Data Augmentation for Light Field Image Super-ResolutionCode0
Image Denoising with Graph-Convolutional Neural NetworksCode0
DALI: Domain Adaptive LiDAR Object Detection via Distribution-level and Instance-level Pseudo Label DenoisingCode0
Automatic Online Multi-Source Domain AdaptationCode0
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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