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 4426–4450 of 7282 papers

TitleStatusHype
PaRa: Personalizing Text-to-Image Diffusion via Parameter Rank Reduction—0
ULF: Cross-Validation for Weak Supervision—0
Parsimonious Labeling—0
PartDiff: Image Super-resolution with Partial Diffusion Models—0
Partially Conditioned Patch Parallelism for Accelerated Diffusion Model Inference—0
Partial Relaxed Optimal Transport for Denoised Recommendation—0
Deep Message Passing on Sets—0
Pasadena: Perceptually Aware and Stealthy Adversarial Denoise Attack—0
Active Speech Enhancement: Active Speech Denoising Decliping and Deveraberation—0
Paste, Inpaint and Harmonize via Denoising: Subject-Driven Image Editing with Pre-Trained Diffusion Model—0
Patch2Self2: Self-supervised Denoising on Coresets via Matrix Sketching—0
Wheelchair automation by a hybrid BCI system using SSVEP and eye blinks—0
UltraPixel: Advancing Ultra-High-Resolution Image Synthesis to New Peaks—0
Patch-based adaptive temporal filter and residual evaluation—0
Patch-Based Denoising Diffusion Probabilistic Model for Sparse-View CT Reconstruction—0
Patch-Based Image Restoration using Expectation Propagation—0
Patch-based Interferometric Phase Estimation via Mixture of Gaussian Density Modelling & Non-local Averaging in the Complex Domain—0
Patch-based learning of adaptive Total Variation parameter maps for blind image denoising—0
Patch-based Non-Local Bayesian Networks for Blind Confocal Microscopy Denoising—0
PatchDIP Exploiting Patch Redundancy in Deep Image Prior for Denoising—0
Patch Group Based Nonlocal Self-Similarity Prior Learning for Image Denoising—0
Patch redundancy in images: a statistical testing framework and some applications—0
PatchRefiner V2: Fast and Lightweight Real-Domain High-Resolution Metric Depth Estimation—0
Patch Triplet Similarity Purification for Guided Real-World Low-Dose CT Image Denoising—0
When deep denoising meets iterative phase retrieval—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