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

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Showing 4276–4300 of 7282 papers

TitleStatusHype
Multi-stage image denoising with the wavelet transformCode1
Enhancing convolutional neural network generalizability via low-rank weight approximation—0
Convergence of score-based generative modeling for general data distributions—0
Analog Image Denoising with an Adaptive Memristive Crossbar Network—0
Transfer learning for self-supervised, blind-spot seismic denoising—0
Conversion Between CT and MRI Images Using Diffusion and Score-Matching Models—0
Robust Hyperspectral Image Fusion with Simultaneous Guide Image Denoising via Constrained Convex Optimization—0
JPEG Artifact Correction using Denoising Diffusion Restoration ModelsCode1
LGDN: Language-Guided Denoising Network for Video-Language Modeling—0
CMGAN: Conformer-Based Metric-GAN for Monaural Speech EnhancementCode2
Sampling is as easy as learning the score: theory for diffusion models with minimal data assumptions—0
MIDMs: Matching Interleaved Diffusion Models for Exemplar-based Image TranslationCode1
Explaining Anomalies using Denoising Autoencoders for Financial Tabular Data—0
Mandarin Singing Voice Synthesis with Denoising Diffusion Probabilistic Wasserstein GAN—0
De-speckling of Optical Coherence Tomography Images Using Anscombe Transform and a Noisier2noise Model—0
NBD-GAP: Non-Blind Image Deblurring Without Clean Target Images—0
Learning Sparse Latent Representations for Generator Model—0
Weak-signal extraction enabled by deep-neural-network denoising of diffraction dataCode1
LGC-Net: A Lightweight Gyroscope Calibration Network for Efficient Attitude EstimationCode0
Deep Variation Prior: Joint Image Denoising and Noise Variance Estimation without Clean Data—0
MetaDIP: Accelerating Deep Image Prior with Meta Learning—0
Deep Plug-and-Play Prior for Hyperspectral Image RestorationCode1
Denoising Diffusion Error Correction Codes—0
Multi-channel Nuclear Norm Minus Frobenius Norm Minimization for Color Image Denoising—0
4DenoiseNet: Adverse Weather Denoising from Adjacent Point CloudsCode1
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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