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 5401–5425 of 7282 papers

TitleStatusHype
A New Data Representation Based on Training Data Characteristics to Extract Drug Named-Entity in Medical Text—0
Attention-Driven Training-Free Efficiency Enhancement of Diffusion Models—0
Attention Overlap Is Responsible for The Entity Missing Problem in Text-to-image Diffusion Models!—0
A Tunable Despeckling Neural Network Stabilized via Diffusion Equation—0
Simultaneous Grouping and Denoising via Sparse Convex Wavelet Clustering—0
A Two-stage Complex Network using Cycle-consistent Generative Adversarial Networks for Speech Enhancement—0
A two-stage learning method for protein-protein interaction prediction—0
A Two-stage Method for Non-extreme Value Salt-and-Pepper Noise Removal—0
Simultaneous Inpainting and Denoising by Directional Global Three-part Decomposition: Connecting Variational and Fourier Domain Based Image Processing—0
Audio Attacks and Defenses against AED Systems -- A Practical Study—0
Audio Classical Composer Identification by Deep Neural Network—0
Audio Denoising for Robust Audio Fingerprinting—0
AUDIT: Audio Editing by Following Instructions with Latent Diffusion Models—0
Augmented Robust PCA For Foreground-Background Separation on Noisy, Moving Camera Video—0
A new ADMM algorithm for the Euclidean median and its application to robust patch regression—0
A Unified Approach to Analysis and Design of Denoising Markov Models—0
A Unified Conditional Framework for Diffusion-based Image Restoration—0
A Unified Framework for Multi-Sensor HDR Video Reconstruction—0
A unified framework of non-local parametric methods for image denoising—0
A Unified Plug-and-Play Framework for Effective Data Denoising and Robust Abstention—0
A Unified View Between Tensor Hypergraph Neural Networks And Signal Denoising—0
A Universal Deep Learning Framework for Real-Time Denoising of Ultrasound Images—0
AuthentiGPT: Detecting Machine-Generated Text via Black-Box Language Models Denoising—0
AutoDenoise: Automatic Data Instance Denoising for Recommendations—0
Autoencoders as Weight Initialization of Deep Classification Networks for Cancer versus Cancer Studies—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