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 4751–4775 of 7282 papers

TitleStatusHype
What cleaves? Is proteasomal cleavage prediction reaching a ceiling?—0
Removing Radio Frequency Interference from Auroral Kilometric Radiation with Stacked AutoencodersCode0
HKF: Hierarchical Kalman Filtering with Online Learned Evolution Priors for Adaptive ECG DenoisingCode0
Diffusion Motion: Generate Text-Guided 3D Human Motion by Diffusion Model—0
Score-based Denoising Diffusion with Non-Isotropic Gaussian Noise Models—0
Speech Dereverberation with a Reverberation Time Shortening TargetCode0
Reversed Image Signal Processing and RAW Reconstruction. AIM 2022 Challenge Report—0
A Magnetic Framelet-Based Convolutional Neural Network for Directed Graphs—0
Video super-resolution for single-photon LIDAR—0
Efficient Bi-Level Optimization for Recommendation DenoisingCode0
Improving Chinese Story Generation via Awareness of Syntactic Dependencies and SemanticsCode0
Denoising Enhanced Distantly Supervised Ultrafine Entity Typing—0
spatial-dccrn: dccrn equipped with frame-level angle feature and hybrid filtering for multi-channel speech enhancement—0
Scale-Agnostic Super-Resolution in MRI using Feature-Based Coordinate Networks—0
Gated Recurrent Unit for Video Denoising—0
Signal Processing for Implicit Neural Representations—0
Robust Graph Filter Identification and Graph Denoising from Signal ObservationsCode0
Adv-Attribute: Inconspicuous and Transferable Adversarial Attack on Face Recognition—0
Dissipative residual layers for unsupervised implicit parameterization of data manifolds—0
Common Corruption Robustness of Point Cloud Detectors: Benchmark and Enhancement—0
Instance Regularization for Discriminative Language Model Pre-trainingCode0
Unsupervised classification of the spectrogram zerosCode0
Retinex Image Enhancement Based on Sequential Decomposition With a Plug-and-Play Framework—0
Evaluating Unsupervised Denoising Requires Unsupervised MetricsCode0
Masked Autoencoders for Low dose CT denoising—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