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 2376–2400 of 7282 papers

TitleStatusHype
Deep Learning Based Channel Covariance Matrix Estimation with User Location and Scene Images—0
Deep Learning Architecture for Motor Imaged Words—0
Equivariant plug-and-play image reconstruction—0
Deep K-SVD Denoising—0
Automatic Tooth Arrangement with Joint Features of Point and Mesh Representations via Diffusion Probabilistic Models—0
Deep Kernelized Autoencoders—0
Automatic Summarization for Creative Writing: BART based Pipeline Method for Generating Summary of Movie Scripts—0
An Equivariant Pretrained Transformer for Unified 3D Molecular Representation Learning—0
Deep Joint Denoising and Detection for Enhanced Intracellular Particle Analysis—0
Deep Iterative Reconstruction for Phase Retrieval—0
Automatic Parameter Selection for Denoising Algorithms Using a No-Reference Measure of Image Content—0
Fast and Accurate: Video Enhancement using Sparse Depth—0
Deep Interference Mitigation and Denoising of Real-World FMCW Radar Signals—0
Adaptive Denoising via GainTuning—0
Cascading Refinement Video Denoising with Uncertainty Adaptivity—0
Deep Interaction between Masking and Mapping Targets for Single-Channel Speech Enhancement—0
Deep Image Priors for Magnetic Resonance Fingerprinting with pretrained Bloch-consistent denoising autoencoders—0
Automatic Muscle Artifacts Identification and Removal from Single-Channel EEG Using Wavelet Transform with Meta-heuristically Optimized Non-local Means Filter—0
Deep Image Destruction: Vulnerability of Deep Image-to-Image Models against Adversarial Attacks—0
Automatic Lumbar Spinal CT Image Segmentation with a Dual Densely Connected U-Net—0
Algorithm Unrolling-based Denoising of Multimodal Graph Signals—0
EP-CFG: Energy-Preserving Classifier-Free Guidance—0
Automatic Image Annotation via Label Transfer in the Semantic Space—0
Deep Identity-aware Transfer of Facial Attributes—0
Automatic generation of realistic training data for learning parallel-jaw grasping from synthetic stereo images—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