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 7176–7200 of 7282 papers

TitleStatusHype
Misaligned Over-The-Air Computation of Multi-Sensor Data with Wiener-Denoiser NetworkCode0
RADU: Ray-Aligned Depth Update Convolutions for ToF Data DenoisingCode0
OpenDenoising: an Extensible Benchmark for Building Comparative Studies of Image DenoisersCode0
Exploring Format Consistency for Instruction TuningCode0
Inexact Derivative-Free Optimization for Bilevel LearningCode0
Exploring Gradient Flow Based Saliency for DNN Model CompressionCode0
Exploring Inter-frequency Guidance of Image for Lightweight Gaussian DenoisingCode0
Missing Data Imputation with Adversarially-trained Graph Convolutional NetworksCode0
Exploring Molecule Generation Using Latent Space Graph DiffusionCode0
Opening the Black Box: Towards inherently interpretable energy data imputation models using building physics insightCode0
Inference Stage Denoising for Undersampled MRI ReconstructionCode0
Inference-Time Diffusion Model DistillationCode0
SC2: Towards Enhancing Content Preservation and Style Consistency in Long Text Style TransferCode0
SCA: Improve Semantic Consistent in Unrestricted Adversarial Attacks via DDPM InversionCode0
Mitigating Label Noise using Prompt-Based Hyperbolic Meta-Learning in Open-Set Domain GeneralizationCode0
A New Multi-Picture Architecture for Learned Video Deinterlacing and Demosaicing with Parallel Deformable Convolution and Self-Attention BlocksCode0
Inferring Neural Signed Distance Functions by Overfitting on Single Noisy Point Clouds through Finetuning Data-Driven based PriorsCode0
A Plug-and-Play Priors Framework for Hyperspectral UnmixingCode0
A Bayesian Perspective on the Deep Image PriorCode0
Symmetric Wasserstein AutoencodersCode0
Exploring Video-Based Driver Activity Recognition under Noisy LabelsCode0
Opportunities and Challenges of Deep Learning Methods for Electrocardiogram Data: A Systematic ReviewCode0
Exposure Bias Reduction for Enhancing Diffusion Transformer Feature CachingCode0
Deep Residual Autoencoders for Expectation Maximization-inspired Dictionary LearningCode0
The Little Engine that Could: Regularization by Denoising (RED)Code0
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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