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 3326–3350 of 7282 papers

TitleStatusHype
Deep Noise Suppression With Non-Intrusive PESQNet Supervision Enabling the Use of Real Training Data—0
Heart Murmur and Abnormal PCG Detection via Wavelet Scattering Transform & a 1D-CNN—0
Illumination and Shadows in Head Rotation: experiments with Denoising Diffusion Models—0
Deep Noise Suppression Maximizing Non-Differentiable PESQ Mediated by a Non-Intrusive PESQNet—0
Bag of Tricks for Effective Language Model Pretraining and Downstream Adaptation: A Case Study on GLUE—0
Head-Neck Dual-energy CT Contrast Media Reduction Using Diffusion Models—0
HDRVideo-GAN: Deep Generative HDR Video Reconstruction—0
Deep Neural Networks to Recover Unknown Physical Parameters from Oscillating Time Series—0
HDR Imaging with Spatially Varying Signal-to-Noise Ratios—0
BADGR: Bundle Adjustment Diffusion Conditioned by GRadients for Wide-Baseline Floor Plan Reconstruction—0
HDR Denoising and Deblurring by Learning Spatio-temporal Distortion Models—0
Heart Rate Extraction from Abdominal Audio Signals—0
Deep neural networks for learning graph representations—0
Heavy-Tailed Diffusion Models—0
HE-Drive: Human-Like End-to-End Driving with Vision Language Models—0
HC^3L-Diff: Hybrid conditional latent diffusion with high frequency enhancement for CBCT-to-CT synthesis—0
HepatoGEN: Generating Hepatobiliary Phase MRI with Perceptual and Adversarial Models—0
Deep neural networks-based denoising models for CT imaging and their efficacy—0
Back to Basics: Fast Denoising Iterative Algorithm—0
All-in-One Deep Learning Framework for MR Image Reconstruction—0
Adaptive Estimation and Learning under Temporal Distribution Shift—0
Accelerated graph-based nonlinear denoising filters—0
Multi-weather Cross-view Geo-localization Using Denoising Diffusion Models—0
Adapting Language Models for Non-Parallel Author-Stylized Rewriting—0
HashTran-DNN: A Framework for Enhancing Robustness of Deep Neural Networks against Adversarial Malware Samples—0
Show:102550
← PrevPage 134 of 292Next →
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