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 3401–3425 of 7282 papers

TitleStatusHype
Enhancing Few-Shot Stock Trend Prediction with Large Language Models—0
HORT: Monocular Hand-held Objects Reconstruction with Transformers—0
Controlled and Conditional Text to Image Generation with Diffusion Prior—0
Artefact removal in ground truth deficient fluctuations-based nanoscopy images using deep learning—0
Enhancing Diffusion Posterior Sampling for Inverse Problems by Integrating Crafted Measurements—0
How Diffusion Models Learn to Factorize and Compose—0
Controllable Satellite-to-Street-View Synthesis with Precise Pose Alignment and Zero-Shot Environmental Control—0
Enhancing Diffusion Models for Inverse Problems with Covariance-Aware Posterior Sampling—0
How Powerful is Implicit Denoising in Graph Neural Networks—0
How should a fixed budget of dwell time be spent in scanning electron microscopy to optimize image quality?—0
Enhancing Diffusion Models for High-Quality Image Generation—0
How to Best Combine Demosaicing and Denoising?—0
How to Construct Energy for Images? Denoising Autoencoder Can Be Energy Based Model—0
How to monitor and mitigate stair-casing in l1 trend filtering—0
SAFE-SIM: Safety-Critical Closed-Loop Traffic Simulation with Diffusion-Controllable Adversaries—0
Array SAR 3D Sparse Imaging Based on Regularization by Denoising Under Few Observed Data—0
How to Unlock Time Series Editing? Diffusion-Driven Approach with Multi-Grained Control—0
How vulnerable is my policy? Adversarial attacks on modern behavior cloning policies—0
Deep Unfolding-Aided Parameter Tuning for Plug-and-Play-Based Video Snapshot Compressive Imaging—0
Controllable Person Image Synthesis with Pose-Constrained Latent Diffusion—0
HSIDMamba: Exploring Bidirectional State-Space Models for Hyperspectral Denoising—0
TexGen: Text-Guided 3D Texture Generation with Multi-view Sampling and Resampling—0
Enhancing CTR Prediction in Recommendation Domain with Search Query Representation—0
HSR-Diff: Hyperspectral Image Super-Resolution via Conditional Diffusion Models—0
Enhancing Crowdsourced Audio for Text-to-Speech Models—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