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 5701–5725 of 7282 papers

TitleStatusHype
The Neural Tangent Link Between CNN Denoisers and Non-Local Filters—0
CNN for License Plate Motion Deblurring—0
Coarse-to-Fine Video Denoising with Dual-Stage Spatial-Channel Transformer—0
CoCo-BERT: Improving Video-Language Pre-training with Contrastive Cross-modal Matching and Denoising—0
CoDe: An Explicit Content Decoupling Framework for Image Restoration—0
Code-Bridged Classifier (CBC): A Low or Negative Overhead Defense for Making a CNN Classifier Robust Against Adversarial Attacks—0
CodeFusion: A Pre-trained Diffusion Model for Code Generation—0
Code Representation Learning At Scale—0
Code-Switching with Word Senses for Pretraining in Neural Machine Translation—0
CoDiPhy: A General Framework for Applying Denoising Diffusion Models to the Physical Layer of Wireless Communication Systems—0
Optimization for Amortized Inverse Problems—0
Coherence and Diversity through Noise: Self-Supervised Paraphrase Generation via Structure-Aware Denoising—0
Self-supervised training of deep denoisers in multi-coil MRI considering noise correlations—0
Soft Autoencoder and Its Wavelet Adaptation Interpretation—0
Anatomically and Metabolically Informed Diffusion for Unified Denoising and Segmentation in Low-Count PET Imaging—0
Collaborative Diffusion Model for Recommender System—0
Collaborative Filtering-Based Method for Low-Resolution and Details Preserving Image Denoising—0
Soft Diffusion: Score Matching for General Corruptions—0
Collaborative Filtering using Denoising Auto-Encoders for Market Basket Data—0
Collaborative Recurrent Autoencoder: Recommend while Learning to Fill in the Blanks—0
Collaborative Total Variation: A General Framework for Vectorial TV Models—0
COLLAGE: Collaborative Human-Agent Interaction Generation using Hierarchical Latent Diffusion and Language Models—0
Color Correction Meets Cross-Spectral Refinement: A Distribution-Aware Diffusion for Underwater Image Restoration—0
ColorEdit: Training-free Image-Guided Color editing with diffusion model—0
Color graph based wavelet transform with perceptual information—0
Show:102550
← PrevPage 229 of 292Next →
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