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 6676–6700 of 7282 papers

TitleStatusHype
On Error Propagation of Diffusion Models—0
Does prior knowledge in the form of multiple low-dose PET images (at different dose levels) improve standard-dose PET prediction?—0
Structure-Enhanced Protein Instruction Tuning: Towards General-Purpose Protein Understanding with LLMs—0
Domain Adaptation based on Human Feedback for Enhancing Generative Model Denoising Abilities—0
Domain Adaptive Neural Networks for Object Recognition—0
Structure-Guided Adversarial Training of Diffusion Models—0
3D Point Cloud Enhancement using Graph-Modelled Multiview Depth Measurements—0
Domain Specific Fine-tuning of Denoising Sequence-to-Sequence Models for Natural Language Summarization—0
Domino Denoise: An Accurate Blind Zero-Shot Denoiser using Domino Tilings—0
DONNAv2 -- Lightweight Neural Architecture Search for Vision tasks—0
Do Noises Bother Human and Neural Networks In the Same Way? A Medical Image Analysis Perspective—0
Don't be so negative! Score-based Generative Modeling with Oracle-assisted Guidance—0
Leveraging Structural Knowledge in Diffusion Models for Source Localization in Data-Limited Graph Scenarios—0
Structure-sensitive Multi-scale Deep Neural Network for Low-Dose CT Denoising—0
DoPAMINE: Double-sided Masked CNN for Pixel Adaptive Multiplicative Noise Despeckling—0
Dose-aware Diffusion Model for 3D PET Image Denoising: Multi-institutional Validation with Reader Study and Real Low-dose Data—0
Studying Classifier(-Free) Guidance From a Classifier-Centric Perspective—0
DoughNet: A Visual Predictive Model for Topological Manipulation of Deformable Objects—0
Do we really have to filter out random noise in pre-training data for language models?—0
DPATD: Dual-Phase Audio Transformer for Denoising—0
Zero-to-Hero: Enhancing Zero-Shot Novel View Synthesis via Attention Map Filtering—0
Style Description based Text-to-Speech with Conditional Prosodic Layer Normalization based Diffusion GAN—0
DP-LET: An Efficient Spatio-Temporal Network Traffic Prediction Framework—0
CLIP4Sketch: Enhancing Sketch to Mugshot Matching through Dataset Augmentation using Diffusion Models—0
ArtWeaver: Advanced Dynamic Style Integration via Diffusion Model—0
Show:102550
← PrevPage 268 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