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 1526–1550 of 7282 papers

TitleStatusHype
Ultrasound Image Enhancement with the Variance of Diffusion ModelsCode1
Exploring Prediction Targets in Masked Pre-Training for Speech Foundation Models—0
Manifold-Constrained Nucleus-Level Denoising Diffusion Model for Structure-Based Drug DesignCode1
Self-Supervised Elimination of Non-Independent Noise in Hyperspectral Imaging—0
Large Language Model Enhanced Hard Sample Identification for Denoising Recommendation—0
Adaptive Segmentation-Based Initialization for Steered Mixture of Experts Image Regression—0
Taming Diffusion Models for Image Restoration: A Review—0
SteeredMarigold: Steering Diffusion Towards Depth Completion of Largely Incomplete Depth MapsCode1
BNEM: A Boltzmann Sampler Based on Bootstrapped Noised Energy MatchingCode0
DiFSD: Ego-Centric Fully Sparse Paradigm with Uncertainty Denoising and Iterative Refinement for Efficient End-to-End Self-DrivingCode1
E1 TTS: Simple and Fast Non-Autoregressive TTS—0
Schrödinger Bridge Flow for Unpaired Data Translation—0
Robust Training of Neural Networks at Arbitrary Precision and Sparsity—0
ManiDext: Hand-Object Manipulation Synthesis via Continuous Correspondence Embeddings and Residual-Guided Diffusion—0
MambaFoley: Foley Sound Generation using Selective State-Space ModelsCode0
Cross-conditioned Diffusion Model for Medical Image to Image Translation—0
xTED: Cross-Domain Adaptation via Diffusion-Based Trajectory EditingCode1
Adjoint Matching: Fine-tuning Flow and Diffusion Generative Models with Memoryless Stochastic Optimal Control—0
Sub-graph Based Diffusion Model for Link Prediction—0
Using Convolutional Neural Networks for Denoising and Deblending of Marine Seismic Data—0
Pushing Joint Image Denoising and Classification to the Edge—0
Localized Schrödinger Bridge Sampler—0
MagicStyle: Portrait Stylization Based on Reference Image—0
Improving Text-guided Object Inpainting with Semantic Pre-inpaintingCode2
Quaternion Nuclear Norm minus Frobenius Norm Minimization for color image reconstruction—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