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 2376–2400 of 7282 papers

TitleStatusHype
ConsistencyDet: A Few-step Denoising Framework for Object Detection Using the Consistency ModelCode0
Considering Image Information and Self-similarity: A Compositional Denoising NetworkCode0
Implicit 3D Orientation Learning for 6D Object Detection from RGB ImagesCode0
Image-to-Image MLP-mixer for Image ReconstructionCode0
Imaging at the quantum limit with convolutional neural networksCode0
Imaging transformer for MRI denoising with the SNR unit training: enabling generalization across field-strengths, imaging contrasts, and anatomyCode0
Connecting Image Denoising and High-Level Vision Tasks via Deep LearningCode0
Conformal Bounds on Full-Reference Image Quality for Imaging Inverse ProblemsCode0
Confidence-aware Denoised Fine-tuning of Off-the-shelf Models for Certified RobustnessCode0
Conditioning diffusion models by explicit forward-backward bridgingCode0
ArchComplete: Autoregressive 3D Architectural Design Generation with Hierarchical Diffusion-Based UpsamplingCode0
Image Segmentation by Iterative Inference from Conditional Score EstimationCode0
Implicit Image-to-Image Schrodinger Bridge for Image RestorationCode0
Image Restoration Using Convolutional Auto-encoders with Symmetric Skip ConnectionsCode0
Image Reconstruction with Predictive Filter FlowCode0
Image Restoration Using Deep Regulated Convolutional NetworksCode0
Image quality measurements and denoising using Fourier Ring CorrelationsCode0
Image Fusion via Sparse Regularization with Non-Convex PenaltiesCode0
A Conditional Denoising Diffusion Probabilistic Model for Radio Interferometric Image ReconstructionCode0
Image Inpainting via Tractable Steering of Diffusion ModelsCode0
Image Restoration using Plug-and-Play CNN MAP DenoisersCode0
Conditional Diffusion Models with Classifier-Free Gibbs-like GuidanceCode0
Image Denoising with Control over Deep Network HallucinationCode0
Image denoising using complex-valued deep CNNCode0
Image denoising using deep CNN with batch renormalizationCode0
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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