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

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Showing 2851–2875 of 7282 papers

TitleStatusHype
An Attention-Based Denoising Framework for Personality Detection in Social Media TextsCode0
DECDM: Document Enhancement using Cycle-Consistent Diffusion Models—0
An adaptive denoising recommendation algorithm for causal separation biasCode0
Generative AI-Based Probabilistic Constellation Shaping With Diffusion Models—0
Synthetically Enhanced: Unveiling Synthetic Data's Potential in Medical Imaging ResearchCode1
Cross-domain feature disentanglement for interpretable modeling of tumor microenvironment impact on drug response—0
DISTA: Denoising Spiking Transformer with intrinsic plasticity and spatiotemporal attention—0
Graph Signal Diffusion Model for Collaborative FilteringCode1
Multiple-Question Multiple-Answer Text-VQA—0
DMV3D: Denoising Multi-View Diffusion using 3D Large Reconstruction Model—0
GEC-DePenD: Non-Autoregressive Grammatical Error Correction with Decoupled Permutation and DecodingCode0
UT5: Pretraining Non autoregressive T5 with unrolled denoising—0
Mustango: Toward Controllable Text-to-Music GenerationCode2
Brain-Driven Representation Learning Based on Diffusion Model—0
Near-Field Sparse Channel Estimation for Extremely Large-Scale RIS-Aided Wireless Communications—0
Robust semi-supervised segmentation with timestep ensembling diffusion models—0
AuthentiGPT: Detecting Machine-Generated Text via Black-Box Language Models Denoising—0
MonoDiffusion: Self-Supervised Monocular Depth Estimation Using Diffusion ModelCode1
An Analysis and Mitigation of the Reversal CurseCode1
One Signal-Noise Separation based Wiener Filter for Magnetogastrogram—0
Physics-Informed Data Denoising for Real-Life Sensing Systems—0
Back to Basics: Fast Denoising Iterative Algorithm—0
ShipGen: A Diffusion Model for Parametric Ship Hull Generation with Multiple Objectives and Constraints—0
Diffusion-Generative Multi-Fidelity Learning for Physical Simulation—0
L-WaveBlock: A Novel Feature Extractor Leveraging Wavelets for Generative Adversarial Networks—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