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 1976–2000 of 7282 papers

TitleStatusHype
DEVDAN: Deep Evolving Denoising Autoencoder—0
Developing Cryptocurrency Trading Strategy Based on Autoencoder-CNN-GANs Algorithms—0
Denoising Gravitational Waves with Enhanced Deep Recurrent Denoising Auto-Encoders—0
DexDiffuser: Generating Dexterous Grasps with Diffusion Models—0
DF-Conformer: Integrated architecture of Conv-TasNet and Conformer using linear complexity self-attention for speech enhancement—0
DFingerNet: Noise-Adaptive Speech Enhancement for Hearing Aids—0
Denoising Gravitational Waves using Deep Learning with Recurrent Denoising Autoencoders—0
Analog Image Denoising with an Adaptive Memristive Crossbar Network—0
Blind2Sound: Self-Supervised Image Denoising without Residual Noise—0
Accelerating Diffusion Models via Pre-segmentation Diffusion Sampling for Medical Image Segmentation—0
Direct Reconstruction of Linear Parametric Images from Dynamic PET Using Nonlocal Deep Image Prior—0
Diagnosing and Preventing Instabilities in Recurrent Video Processing—0
Bringing together invertible UNets with invertible attention modules for memory-efficient diffusion models—0
Diagnostic Quality Assessment for Low-Dimensional ECG Representations—0
Direct Unsupervised Denoising—0
Denoising Functional Maps: Diffusion Models for Shape Correspondence—0
DICE: Diverse Diffusion Model with Scoring for Trajectory Prediction—0
Dictionary Learning and Sparse Coding-based Denoising for High-Resolution Task Functional Connectivity MRI Analysis—0
Dictionary Learning Based on Sparse Distribution Tomography—0
Dictionary Learning Under Generative Coefficient Priors with Applications to Compression—0
Dictionary Learning with Equiprobable Matching Pursuit—0
BLADE: Filter Learning for General Purpose Computational Photography—0
Diff-2-in-1: Bridging Generation and Dense Perception with Diffusion Models—0
Denoising Fisher Training For Neural Implicit Samplers—0
Denoising Fast X-Ray Fluorescence Raster Scans of Paintings—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