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 4001–4025 of 7282 papers

TitleStatusHype
Diffusion-Based Signed Distance Fields for 3D Shape Generation—0
CutMIB: Boosting Light Field Super-Resolution via Multi-View Image BlendingCode1
Deep Random Projector: Accelerated Deep Image PriorCode1
PC2: Projection-Conditioned Point Cloud Diffusion for Single-Image 3D Reconstruction—0
Polarized Color Image Denoising—0
Physics-Guided ISO-Dependent Sensor Noise Modeling for Extreme Low-Light PhotographyCode1
High-Resolution Image Reconstruction With Latent Diffusion Models From Human Brain ActivityCode3
Structure Aggregation for Cross-Spectral Stereo Image Guided DenoisingCode1
Joint HDR Denoising and Fusion: A Real-World Mobile HDR Image DatasetCode1
NeuralLift-360: Lifting an In-the-Wild 2D Photo to a 3D Object With 360deg Views—0
sRGB Real Noise Synthesizing With Neighboring Correlation-Aware Noise ModelCode1
DIVA: Deep Unfolded Network from Quantum Interactive Patches for Image Restoration—0
Hair and Scalp Disease Detection using Machine Learning and Image Processing—0
Image Embedding for Denoising Generative ModelsCode0
Blind Restoration of Real-World Audio by 1D Operational GANsCode0
ResGrad: Residual Denoising Diffusion Probabilistic Models for Text to SpeechCode1
S2S-WTV: Seismic Data Noise Attenuation Using Weighted Total Variation Regularized Self-Supervised Learning—0
ProgNet: A Transferable Deep Network for Aircraft Engine Damage Propagation Prognosis under Real Flight ConditionsCode0
Text Generation with Diffusion Language Models: A Pre-training Approach with Continuous Paragraph Denoise—0
Group Sparse Coding for Image Denoising—0
Enhancing the prediction of disease outcomes using electronic health records and pretrained deep learning models—0
Audio Denoising for Robust Audio Fingerprinting—0
A Non-Asymptotic Analysis of Oversmoothing in Graph Neural Networks—0
On-the-fly Denoising for Data Augmentation in Natural Language UnderstandingCode0
Sophisticated deep learning with on-chip optical diffractive tensor processing—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