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 3576–3600 of 7282 papers

TitleStatusHype
CollaFuse: Navigating Limited Resources and Privacy in Collaborative Generative AICode0
Point Processes and spatial statistics in time-frequency analysis—0
ROG_PL: Robust Open-Set Graph Learning via Region-Based Prototype Learning—0
Balancing Act: Distribution-Guided Debiasing in Diffusion Models—0
Objective and Interpretable Breast Cosmesis Evaluation with Attention Guided Denoising Diffusion Anomaly Detection Model—0
Self-Supervised Learning with Generative Adversarial Networks for Electron Microscopy—0
Multi-Scale Denoising in the Feature Space for Low-Light Instance Segmentation—0
Denoising Diffusion Models for Inpainting of Healthy Brain Tissue—0
RISAR: RIS-assisted Human Activity Recognition with Commercial Wi-Fi Devices—0
Structure-Guided Adversarial Training of Diffusion Models—0
Seeing and Hearing: Open-domain Visual-Audio Generation with Diffusion Latent Aligners—0
Zeroth-Order Sampling Methods for Non-Log-Concave Distributions: Alleviating Metastability by Denoising DiffusionCode0
Generative AI in Vision: A Survey on Models, Metrics and Applications—0
GEM3D: GEnerative Medial Abstractions for 3D Shape Synthesis—0
Multi-LoRA Composition for Image Generation—0
Photon-counting CT using a Conditional Diffusion Model for Super-resolution and Texture-preservation—0
IRConStyle: Image Restoration Framework Using Contrastive Learning and Style TransferCode0
Frustratingly Simple Prompting-based Text Denoising—0
Pretraining Strategy for Neural PotentialsCode0
On normalization-equivariance properties of supervised and unsupervised denoising methods: a survey—0
Background Denoising for Ptychography via Wigner Distribution Deconvolution—0
A Study of Shape Modeling Against Noise—0
Label-efficient Multi-organ Segmentation Method with Diffusion Model—0
PeriodGrad: Towards Pitch-Controllable Neural Vocoder Based on a Diffusion Probabilistic Model—0
Mel-FullSubNet: Mel-Spectrogram Enhancement for Improving Both Speech Quality and ASR—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