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 6801–6825 of 7282 papers

TitleStatusHype
LLNet: A Deep Autoencoder Approach to Natural Low-light Image EnhancementCode0
A Two-stage Deep Network for High Dynamic Range Image ReconstructionCode0
Test-Time Scaling of Diffusion Models via Noise Trajectory SearchCode0
LMD: Faster Image Reconstruction with Latent Masking DiffusionCode0
Robust Simultaneous Multislice MRI Reconstruction Using Deep Generative PriorsCode0
Principal Components for Neural Network InitializationCode0
Deep Decoder: Concise Image Representations from Untrained Non-convolutional NetworksCode0
AIM2PC: Aerial Image to 3D Building Point Cloud ReconstructionCode0
An Empirical Review of Adversarial DefensesCode0
RL-based Stateful Neural Adaptive Sampling and Denoising for Real-Time Path TracingCode0
Holistic Guidance for Occluded Person Re-IdentificationCode0
Localized Fourier Analysis for Graph Signal ProcessingCode0
BrainCodec: Neural fMRI codec for the decoding of cognitive brain statesCode0
An Attention-Based Denoising Framework for Personality Detection in Social Media TextsCode0
Sub2Full: split spectrum to boost OCT despeckling without clean dataCode0
Improving Non-Intrusive Load Disaggregation through an Attention-Based Deep Neural NetworkCode0
Non-linear aggregation of filters to improve image denoisingCode0
Unsupervised Learning with Stein's Unbiased Risk EstimatorCode0
Context-aware Deep Feature Compression for High-speed Visual TrackingCode0
Dreaming of Electrical Waves: Generative Modeling of Cardiac Excitation Waves using Diffusion ModelsCode0
How Control Information Influences Multilingual Text Image Generation and Editing?Code0
Linear Combinations of Patches are Unreasonably Effective for Single-Image DenoisingCode0
DreamSteerer: Enhancing Source Image Conditioned Editability using Personalized Diffusion ModelsCode0
How Does Diffusion Influence Pretrained Language Models on Out-of-Distribution Data?Code0
LoFi: Neural Local Fields for Scalable Image ReconstructionCode0
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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