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 4876–4900 of 7282 papers

TitleStatusHype
Zero-Shot Paraphrase Generation with Multilingual Language Models—0
Unsupervised Detection of Fetal Brain Anomalies using Denoising Diffusion Models—0
ResWCAE: Biometric Pattern Image Denoising Using Residual Wavelet-Conditioned Autoencoder—0
Rethinking Circuit Completeness in Language Models: AND, OR, and ADDER Gates—0
Rethinking Class Activation Maps for Segmentation: Revealing Semantic Information in Shallow Layers by Reducing Noise—0
Graph Harmony: Denoising and Nuclear-Norm Wasserstein Adaptation for Enhanced Domain Transfer in Graph-Structured Data—0
Unsupervised Domain Adaptation for ToF Data Denoising With Adversarial Learning—0
Unsupervised Domain Adaptation for Word Sense Disambiguation using Stacked Denoising Autoencoder—0
Unsupervised Fingerphoto Presentation Attack Detection With Diffusion Models—0
Rethinking Pseudo-Label Guided Learning for Weakly Supervised Temporal Action Localization from the Perspective of Noise Correction—0
Unsupervised Graph Spectral Feature Denoising for Crop Yield Prediction—0
Rethinking Weak Supervision in Helping Contrastive Learning—0
Zero-Shot Personalized Speech Enhancement through Speaker-Informed Model Selection—0
Retinal OCT Denoising with Pseudo-Multimodal Fusion Network—0
Retinal OCT Synthesis with Denoising Diffusion Probabilistic Models for Layer Segmentation—0
Retinex-based Image Denoising / Contrast Enhancement using Gradient Graph Laplacian Regularizer—0
Retinex Image Enhancement Based on Sequential Decomposition With a Plug-and-Play Framework—0
Unsupervised Hyperspectral Mixed Noise Removal Via Spatial-Spectral Constrained Deep Image Prior—0
ReToMe-VA: Recursive Token Merging for Video Diffusion-based Unrestricted Adversarial Attack—0
Retrieval Augmented Diffusion Model for Structure-informed Antibody Design and Optimization—0
ACCORD: Alleviating Concept Coupling through Dependence Regularization for Text-to-Image Diffusion Personalization—0
Retrieval Term Prediction Using Deep Learning Methods—0
Revealing Directions for Text-guided 3D Face Editing—0
Revealing higher-order neural representations of uncertainty with the Noise Estimation through Reinforcement-based Diffusion (NERD) model—0
Reversed Image Signal Processing and RAW Reconstruction. AIM 2022 Challenge Report—0
Show:102550
← PrevPage 196 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