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

Showing 10011025 of 7282 papers

TitleStatusHype
Consistent Diffusion: Denoising Diffusion Model with Data-Consistent Training for Image Restoration0
Guided and Variance-Corrected Fusion with One-shot Style Alignment for Large-Content Image GenerationCode0
Machine Learning-Based Automated Assessment of Intracorporeal Suturing in Laparoscopic Fundoplication0
Quantifying Climate Change Impacts on Renewable Energy Generation: A Super-Resolution Recurrent Diffusion Model0
Bilevel Learning with Inexact Stochastic GradientsCode0
Probabilities-Informed Machine Learning0
Re-Attentional Controllable Video Diffusion EditingCode1
MOVIS: Enhancing Multi-Object Novel View Synthesis for Indoor Scenes0
SPADE: Spectroscopic Photoacoustic Denoising using an Analytical and Data-free Enhancement Framework0
3D^2-Actor: Learning Pose-Conditioned 3D-Aware Denoiser for Realistic Gaussian Avatar ModelingCode1
AsymRnR: Video Diffusion Transformers Acceleration with Asymmetric Reduction and Restoration0
Spatiotemporal Blind-Spot Network with Calibrated Flow Alignment for Self-Supervised Video DenoisingCode1
Missing data imputation for noisy time-series data and applications in healthcare0
DynamicScaler: Seamless and Scalable Video Generation for Panoramic Scenes0
OTLRM: Orthogonal Learning-based Low-Rank Metric for Multi-Dimensional Inverse ProblemsCode1
Fast and Robust Visuomotor Riemannian Flow Matching Policy0
LAN: Learning to Adapt Noise for Image DenoisingCode1
Generative Modeling with DiffusionCode0
Rapid Reconstruction of Extremely Accelerated Liver 4D MRI via Chained Iterative Refinement0
Diffusion-based Method for Satellite Pattern-of-Life Identification0
Diffusion Model from Scratch0
Zigzag Diffusion Sampling: Diffusion Models Can Self-Improve via Self-ReflectionCode2
SoftVQ-VAE: Efficient 1-Dimensional Continuous TokenizerCode3
SnapGen-V: Generating a Five-Second Video within Five Seconds on a Mobile Device0
FM2S: Towards Spatially-Correlated Noise Modeling in Zero-Shot Fluorescence Microscopy Image DenoisingCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1SINDyPSNR81Unverified
2Pixel-shuffling DownsamplingPSNR38.4Unverified
3TWSCPSNR37.93Unverified
4CBDNet(Syn)PSNR37.57Unverified
5MCWNNMPSNR37.38Unverified
6Han et alPSNR35.95Unverified
7FFDNetPSNR34.4Unverified
8TNRDPSNR33.65Unverified
9CDnCNN-BPSNR32.43Unverified
10NLRNPSNR30.8Unverified
#ModelMetricClaimedVerifiedStatus
1DRUnet_Poisson_0.01Average PSNR (dB)33.92Unverified
#ModelMetricClaimedVerifiedStatus
1DRANetAverage PSNR39.64Unverified
#ModelMetricClaimedVerifiedStatus
1PCNN+RL+HMEAverage84.61Unverified