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 2751–2775 of 7282 papers

TitleStatusHype
Focusing on what to decode and what to train: SOV Decoding with Specific Target Guided DeNoising and Vision Language AdvisorCode0
Diffusion Counterfactuals for Image RegressorsCode0
A Design Methodology for Efficient Implementation of Deconvolutional Neural Networks on an FPGACode0
FPD-M-net: Fingerprint Image Denoising and Inpainting Using M-Net Based Convolutional Neural NetworksCode0
Flashlight CNN Image DenoisingCode0
Counterfactual MRI Data Augmentation using Conditional Denoising Diffusion Generative ModelsCode0
Novel Hybrid Integrated Pix2Pix and WGAN Model with Gradient Penalty for Binary Images DenoisingCode0
NTIRE 2020 Challenge on Real Image Denoising: Dataset, Methods and ResultsCode0
FlexControl: Computation-Aware ControlNet with Differentiable Router for Text-to-Image GenerationCode0
Observation Denoising in CYRUS Soccer Simulation 2D Team For RoboCup 2024Code0
First line of defense: A robust first layer mitigates adversarial attacksCode0
Fingerprint Presentation Attack Detection by Channel-wise Feature DenoisingCode0
CETA: A Consensus Enhanced Training Approach for Denoising in Distantly Supervised Relation ExtractionCode0
FiRe: Fixed-points of Restoration Priors for Solving Inverse ProblemsCode0
First image then video: A two-stage network for spatiotemporal video denoisingCode0
Diffusion-Based Failure Sampling for Evaluating Safety-Critical Autonomous SystemsCode0
Certification of Deep Learning Models for Medical Image SegmentationCode0
Fine-grained Forecasting Models Via Gaussian Process Blurring EffectCode0
Fine-grained Contrastive Learning for Relation ExtractionCode0
Generalization through variance: how noise shapes inductive biases in diffusion modelsCode0
FFDNet: Toward a Fast and Flexible Solution for CNN based Image DenoisingCode0
Few-shot point cloud reconstruction and denoising via learned Guassian splats renderings and fine-tuned diffusion featuresCode0
Unsupervisedly Prompting AlphaFold2 for Few-Shot Learning of Accurate Folding Landscape and Protein Structure PredictionCode0
Expressive Score-Based Priors for Distribution Matching with Geometry-Preserving RegularizationCode0
FEUNet: a flexible and effective U-shaped network for image denoisingCode0
Show:102550
← PrevPage 111 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