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 7151–7175 of 7282 papers

TitleStatusHype
FlexiAct: Towards Flexible Action Control in Heterogeneous Scenarios—0
Understanding and Tackling Scattering and Reflective Flare for Mobile Camera Systems—0
Tailoring Frictional Properties of Surfaces Using Diffusion Models—0
Flexible Multi-layer Sparse Approximations of Matrices and Applications—0
FlexiDiT: Your Diffusion Transformer Can Easily Generate High-Quality Samples with Less Compute—0
Flexiffusion: Segment-wise Neural Architecture Search for Flexible Denoising Schedule—0
Taking a Big Step: Large Learning Rates in Denoising Score Matching Prevent Memorization—0
TALE: Training-free Cross-domain Image Composition via Adaptive Latent Manipulation and Energy-guided Optimization—0
FLLIC: Functionally Lossless Image Compression—0
A Fused Deep Denoising Sound Coding Strategy for Bilateral Cochlear Implants—0
A Framework for CSI-Based Indoor Localization with 1D Convolutional Neural Networks—0
A framework for conditional diffusion modelling with applications in motif scaffolding for protein design—0
FlowLoss: Dynamic Flow-Conditioned Loss Strategy for Video Diffusion Models—0
Taming Consistency Distillation for Accelerated Human Image Animation—0
FlowRAM: Grounding Flow Matching Policy with Region-Aware Mamba Framework for Robotic Manipulation—0
Taming Diffusion Models for Image Restoration: A Review—0
A Fourier Disparity Layer representation for Light Fields—0
Fluctuation-based deconvolution in fluorescence microscopy using plug-and-play denoisers—0
LDM-ISP: Enhancing Neural ISP for Low Light with Latent Diffusion Models—0
fMRI-based Static and Dynamic Functional Connectivity Analysis for Post-stroke Motor Dysfunction Patient: A Review—0
FN-Net:Remove the Outliers by Filtering the Noise—0
FocusDiffuser: Perceiving Local Disparities for Camouflaged Object Detection—0
Taming Score-Based Diffusion Priors for Infinite-Dimensional Nonlinear Inverse Problems—0
FollowGen: A Scaled Noise Conditional Diffusion Model for Car-Following Trajectory Prediction—0
A Fokker-Planck-Based Loss Function that Bridges Dynamics with Density Estimation—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