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 126–150 of 7282 papers

TitleStatusHype
Optimal Density Functions for Weighted Convolution in Learning ModelsCode2
D-AR: Diffusion via Autoregressive ModelsCode2
DiSA: Diffusion Step Annealing in Autoregressive Image GenerationCode2
Training-Free Multi-Step Audio Source SeparationCode2
Improved Immiscible Diffusion: Accelerate Diffusion Training by Reducing Its MiscibilityCode2
dKV-Cache: The Cache for Diffusion Language ModelsCode2
Degradation-Aware Feature Perturbation for All-in-One Image RestorationCode2
Non-stationary Diffusion For Probabilistic Time Series ForecastingCode2
Noise Modeling in One Hour: Minimizing Preparation Efforts for Self-supervised Low-Light RAW Image DenoisingCode2
Prior Does Matter: Visual Navigation via Denoising Diffusion Bridge ModelsCode2
Gaussian Mixture Flow Matching ModelsCode2
On-device Sora: Enabling Training-Free Diffusion-based Text-to-Video Generation for Mobile DevicesCode2
Effective Cloud Removal for Remote Sensing Images by an Improved Mean-Reverting Denoising Model with Elucidated Design SpaceCode2
DynamiCtrl: Rethinking the Basic Structure and the Role of Text for High-quality Human Image AnimationCode2
Riemannian Optimization on Relaxed Indicator Matrix ManifoldCode2
Dita: Scaling Diffusion Transformer for Generalist Vision-Language-Action PolicyCode2
DnLUT: Ultra-Efficient Color Image Denoising via Channel-Aware Lookup TablesCode2
SALAD: Skeleton-aware Latent Diffusion for Text-driven Motion Generation and EditingCode2
Towards Better Alignment: Training Diffusion Models with Reinforcement Learning Against Sparse RewardsCode2
D2GV: Deformable 2D Gaussian Splatting for Video Representation in 400FPSCode2
Geodesic Diffusion Models for Medical Image-to-Image GenerationCode2
What Makes a Good Diffusion Planner for Decision Making?Code2
Mobius: Text to Seamless Looping Video Generation via Latent ShiftCode2
CleanMel: Mel-Spectrogram Enhancement for Improving Both Speech Quality and ASRCode2
MAGO-SP: Detection and Correction of Water-Fat Swaps in Magnitude-Only VIBE MRICode2
Show:102550
← PrevPage 6 of 292Next →
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