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 5426–5450 of 7282 papers

TitleStatusHype
Auto-encoders: reconstruction versus compression—0
Autoencoding Random Forests—0
Automated Atrial Fibrillation Classification Based on Denoising Stacked Autoencoder and Optimized Deep Network—0
Automated Channel Pruning with Learned Importance—0
Automated Data Denoising for Recommendation—0
Automated Learning of Semantic Embedding Representations for Diffusion Models—0
Automated OCT Segmentation for Images with DME—0
Simultaneous reconstruction and displacement estimation for spectral-domain optical coherence elastography—0
Automatic artifact removal of resting-state fMRI with Deep Neural Networks—0
Automatic Detection of ECG Abnormalities by using an Ensemble of Deep Residual Networks with Attention—0
Automatic Diagnosis of Myocarditis Disease in Cardiac MRI Modality using Deep Transformers and Explainable Artificial Intelligence—0
Simultaneous Sparse Dictionary Learning and Pruning—0
Automatic Foreground Extraction from Imperfect Backgrounds using Multi-Agent Consensus Equilibrium—0
Automatic generation of realistic training data for learning parallel-jaw grasping from synthetic stereo images—0
Automatic Image Annotation via Label Transfer in the Semantic Space—0
Automatic Lumbar Spinal CT Image Segmentation with a Dual Densely Connected U-Net—0
Automatic Muscle Artifacts Identification and Removal from Single-Channel EEG Using Wavelet Transform with Meta-heuristically Optimized Non-local Means Filter—0
Automatic Parameter Selection for Denoising Algorithms Using a No-Reference Measure of Image Content—0
Automatic Summarization for Creative Writing: BART based Pipeline Method for Generating Summary of Movie Scripts—0
Automatic Tooth Arrangement with Joint Features of Point and Mesh Representations via Diffusion Probabilistic Models—0
Autonomous Deep Learning: Incremental Learning of Denoising Autoencoder for Evolving Data Streams—0
Autonomous Point Cloud Segmentation for Power Lines Inspection in Smart Grid—0
Simultaneous Tensor Completion and Denoising by Noise Inequality Constrained Convex Optimization—0
Autoregressive Moving Average Graph Filtering—0
Autoregressive Score Matching—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