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 4826–4850 of 7282 papers

TitleStatusHype
Zero-Shot Noise2Noise: Efficient Image Denoising without any Data—0
A Comparative Study of Image Denoising Algorithms—0
Unsupervised Anomaly Detection Using Diffusion Trend Analysis—0
A Comparative Study of Filtering Approaches Applied to Color Archival Document Images—0
Removing Adversarial Noise in Class Activation Feature Space—0
Removing Anomalies as Noises for Industrial Defect Localization—0
Unsupervised Bilingual Word Embedding Agreement for Unsupervised Neural Machine Translation—0
Removing Rain From Single Images via a Deep Detail Network—0
A Comparative Study for the Nuclear Norms Minimization Methods—0
A Comparative Evaluation of Deep Learning Models for Speech Enhancement in Real-World Noisy Environments—0
Unsupervised Coordinate-Based Video Denoising—0
A Chebyshev Confidence Guided Source-Free Domain Adaptation Framework for Medical Image Segmentation—0
Representation, Analysis of Bayesian Refinement Approximation Network: A Survey—0
Representation Learning based and Interpretable Reactor System Diagnosis Using Denoising Padded Autoencoder—0
Representation Learning for Compressed Video Action Recognition via Attentive Cross-modal Interaction with Motion Enhancement—0
Representation Learning for Resource-Constrained Keyphrase Generation—0
Unsupervised CP-UNet Framework for Denoising DAS Data with Decay Noise—0
Representation Learning for Spatial Graphs—0
Diffusion-Based Representation Learning—0
Representation Learning in Continuous-Time Score-Based Generative Models—0
Self-supervised Deep Unrolled Reconstruction Using Regularization by Denoising—0
Representative Feature Extraction During Diffusion Process for Sketch Extraction with One Example—0
Representing and Denoising Wearable ECG Recordings—0
Polar Encoding: A Simple Baseline Approach for Classification with Missing Values—0
WISVA: Generative AI for 5G Network Optimization in Smart Warehouses—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