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 4651–4675 of 7282 papers

TitleStatusHype
Proposition of a Theoretical Model for Missing Data Imputation using Deep Learning and Evolutionary Algorithms—0
Unifying and extending Diffusion Models through PDEs for solving Inverse Problems—0
ProSE: Diffusion Priors for Speech Enhancement—0
Why are deep nets reversible: A simple theory, with implications for training—0
UniHIST: A Unified Framework for Image Restoration With Marginal Histogram Constraints—0
Protein Structure and Sequence Generation with Equivariant Denoising Diffusion Probabilistic Models—0
A Comprehensive Survey on Image Signal Processing Approaches for Low-Illumination Image Enhancement—0
ProtInvTree: Deliberate Protein Inverse Folding with Reward-guided Tree Search—0
Deep Autoencoders: From Understanding to Generalization Guarantees—0
Provable Preconditioned Plug-and-Play Approach for Compressed Sensing MRI Reconstruction—0
UNIMO-G: Unified Image Generation through Multimodal Conditional Diffusion—0
Provably robust estimation of modulo 1 samples of a smooth function with applications to phase unwrapping—0
Why current rain denoising models fail on CycleGAN created rain images in autonomous driving—0
Proximal Deep Structured Models—0
Proximal Gradient Algorithms: Applications in Signal Processing—0
Proximal Splitting Networks for Image Restoration—0
A Comprehensive Survey of Transformers for Computer Vision—0
Pruned non-local means—0
PSDiff: Diffusion Model for Person Search with Iterative and Collaborative Refinement—0
Pseudo 3D Auto-Correlation Network for Real Image Denoising—0
Low Rank Regularization: A Review—0
PSF-4D: A Progressive Sampling Framework for View Consistent 4D Editing—0
Psi-Sampler: Initial Particle Sampling for SMC-Based Inference-Time Reward Alignment in Score Models—0
UNIT-DDPM: UNpaired Image Translation with Denoising Diffusion Probabilistic Models—0
Psychoacoustic Challenges Of Speech Enhancement On VoIP Platforms—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