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 5001–5025 of 7282 papers

TitleStatusHype
Improved far-field speech recognition using Joint Variational Autoencoder—0
Signal Recovery with Non-Expansive Generative Network Priors—0
Capturing the Denoising Effect of PCA via Compression Ratio—0
Parametric Level-sets Enhanced To Improve Reconstruction (PaLEnTIR)—0
Denoising of Three-Dimensional Fast Spin Echo Magnetic Resonance Images of Knee Joints using Spatial-Variant Noise-Relevant Residual Learning of Convolution Neural Network—0
A Mask-Based Adversarial Defense Scheme—0
Speech Dereverberation with A Reverberation Time Shortening Target—0
DiffMD: A Geometric Diffusion Model for Molecular Dynamics Simulations—0
Learning Enriched Features for Fast Image Restoration and Enhancement—0
A qualitative investigation of optical flow algorithms for video denoising—0
Dynamic Point Cloud Denoising via Gradient Fields—0
Shallow camera pipeline for night photography rendering—0
Partial Relaxed Optimal Transport for Denoised Recommendation—0
Fast Multi-grid Methods for Minimizing Curvature EnergyCode0
Sapinet: A sparse event-based spatiotemporal oscillator for learning in the wild—0
METRO: Efficient Denoising Pretraining of Large Scale Autoencoding Language Models with Model Generated Signals—0
SUMD: Super U-shaped Matrix Decomposition Convolutional neural network for Image denoising—0
Negligible effect of brain MRI data preprocessing for tumor segmentationCode0
Self-Supervised Audio-and-Text Pre-training with Extremely Low-Resource Parallel DataCode0
NAN: Noise-Aware NeRFs for Burst-Denoising—0
Denoising Neural Network for News Recommendation with Positive and Negative Implicit Feedback—0
Motion Artifacts Correction from Single-Channel EEG and fNIRS Signals using Novel Wavelet Packet Decomposition in Combination with Canonical Correlation Analysis—0
Real Image Denoising With a Locally-Adaptive Bitonic Filter—0
Underwater Image Enhancement Using Pre-trained Transformer—0
Dancing under the stars: video denoising in starlight—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