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 5876–5900 of 7282 papers

TitleStatusHype
Sparse Bayesian Dictionary Learning with a Gaussian Hierarchical Model—0
Correction by Projection: Denoising Images with Generative Adversarial Networks—0
Correlation Distance Skip Connection Denoising Autoencoder (CDSK-DAE) for Speech Feature Enhancement—0
Correlation Preserving Sparse Coding Over Multi-level Dictionaries for Image Denoising—0
Corrupting Data to Remove Deceptive Perturbation: Using Preprocessing Method to Improve System Robustness—0
Cost-Aware Routing for Efficient Text-To-Image Generation—0
Sparse Coding on Cascaded Residuals—0
CoT-lized Diffusion: Let's Reinforce T2I Generation Step-by-step—0
CountDiffusion: Text-to-Image Synthesis with Training-Free Counting-Guidance Diffusion—0
Robustifying Models Against Adversarial Attacks by Langevin Dynamics—0
Counting Guidance for High Fidelity Text-to-Image Synthesis—0
Coupled Analysis Dictionary Learning to inductively learn inversion: Application to real-time reconstruction of Biomedical signals—0
Coupling BM3D with directional wavelet packets for image denoising—0
Covariance Trees for 2D and 3D Processing—0
Variational Self-Supervised Learning—0
Sparse-Denoising Methods for Extracting Desaturation Transients in Cerebral Oxygenation Signals of Preterm Infants—0
Sparse Dictionary Learning for Image Recovery by Iterative Shrinkage—0
Crafting Parts for Expressive Object Composition—0
Creatively Upscaling Images with Global-Regional Priors—0
CRISPnet: Color Rendition ISP Net—0
Critical Iterative Denoising: A Discrete Generative Model Applied to Graphs—0
Critically Damped Third-Order Langevin Dynamics—0
An ambient denoising method based on multi-channel non-negative matrix factorization for wheezing detection—0
Cross-boosting of WNNM Image Denoising method by Directional Wavelet Packets—0
Cross-conditioned Diffusion Model for Medical Image to Image Translation—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