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 4376–4400 of 7282 papers

TitleStatusHype
Optimising image capture for low-light widefield quantitative fluorescence microscopy—0
Optimization-Based Image Restoration under Implementation Constraints in Optical Analog Circuits—0
Optimization of Clustering for Clustering-based Image Denoising—0
Optimizing a Transformer-based network for a deep learning seismic processing workflow—0
Optimizing Diffusion Noise Can Serve As Universal Motion Priors—0
Optimizing Few-Step Diffusion Samplers by Gradient Descent—0
S2-Track: A Simple yet Strong Approach for End-to-End 3D Multi-Object Tracking—0
UB-FineNet: Urban Building Fine-grained Classification Network for Open-access Satellite Images—0
Optimizing k in kNN Graphs with Graph Learning Perspective—0
Optimizing Noise Schedules of Generative Models in High Dimensionss—0
Optimizing OOD Detection in Molecular Graphs: A Novel Approach with Diffusion Models—0
Optimizing Rate-Distortion Performance of Motion Compensated Wavelet Lifting with Denoised Prediction and Update—0
Optimizing the image correction pipeline for pedestrian detection in the thermal-infrared domain—0
OptimOTU: Taxonomically aware OTU clustering with optimized thresholds and a bioinformatics workflow for metabarcoding data—0
Optimum Codesign for Image Denoising Between Type-2 Fuzzy Identifier and Matrix Completion Denoiser—0
Optimum window length of Savitzky-Golay filters with arbitrary order—0
OptShrink: An algorithm for improved low-rank signal matrix denoising by optimal, data-driven singular value shrinkage—0
Oracle Bone Inscriptions Multi-modal Dataset—0
Adaptive Rates for Total Variation Image Denoising—0
Scrambled Translation Problem: A Problem of Denoising UNMT—0
ORL-LDM: Offline Reinforcement Learning Guided Latent Diffusion Model Super-Resolution Reconstruction—0
Orthogonal Constrained Minimization with Tensor _2,p Regularization for HSI Denoising and Destriping—0
Orthogonal Features-based EEG Signal Denoising using Fractionally Compressed AutoEncoder—0
Orthogonal Features Based EEG Signals Denoising Using Fractional and Compressed One-Dimensional CNN AutoEncoder—0
OSDM-MReg: Multimodal Image Registration based One Step Diffusion Model—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