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 3576–3600 of 7282 papers

TitleStatusHype
Blockchain Transaction Fee Forecasting: A Comparison of Machine Learning MethodsCode0
On enhancing the robustness of Vision Transformers: Defensive DiffusionCode0
Poisson-Gaussian Holographic Phase Retrieval with Score-based Image Prior—0
Improving Cascaded Unsupervised Speech Translation with Denoising Back-translation—0
Uncertainty Estimation and Out-of-Distribution Detection for Deep Learning-Based Image Reconstruction using the Local Lipschitz—0
Automated Data Denoising for Recommendation—0
CoMoSpeech: One-Step Speech and Singing Voice Synthesis via Consistency ModelCode2
M4Raw: A multi-contrast, multi-repetition, multi-channel MRI k-space dataset for low-field MRI researchCode1
Position Bias Estimation with Item Embedding for Sparse Dataset—0
Diffusion-based Signal Refiner for Speech Separation—0
A Self-Training Framework Based on Multi-Scale Attention Fusion for Weakly Supervised Semantic SegmentationCode0
Relightify: Relightable 3D Faces from a Single Image via Diffusion Models—0
DifFIQA: Face Image Quality Assessment Using Denoising Diffusion Probabilistic ModelsCode1
Multi-Granularity Denoising and Bidirectional Alignment for Weakly Supervised Semantic SegmentationCode0
Multi-Object Self-Supervised Depth DenoisingCode0
Improving Implicit Feedback-Based Recommendation through Multi-Behavior AlignmentCode1
Style-A-Video: Agile Diffusion for Arbitrary Text-based Video Style TransferCode1
Echo from noise: synthetic ultrasound image generation using diffusion models for real image segmentationCode1
Stochastic Texture Filtering—0
SwinIA: Self-Supervised Blind-Spot Image Denoising without Convolutions—0
3DInvNet: A Deep Learning-Based 3D Ground-Penetrating Radar Data InversionCode1
Autoencoder-based prediction of ICU clinical codesCode0
DiffBFR: Bootstrapping Diffusion Model Towards Blind Face Restoration—0
Multi-Scale Energy (MuSE) plug and play framework for inverse problems—0
Real-World Denoising via Diffusion Model—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