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 2576–2600 of 7282 papers

TitleStatusHype
A Topological Loss Function: Image Denoising on a Low-Light Dataset—0
A global optimization SAR image segmentation model can be easily transformed to a general ROF denoising model—0
AB-Cache: Training-Free Acceleration of Diffusion Models via Adams-Bashforth Cached Feature Reuse—0
DAPAS : Denoising Autoencoder to Prevent Adversarial attack in Semantic Segmentation—0
Dantzig Selector with an Approximately Optimal Denoising Matrix and its Application to Reinforcement Learning—0
A Time-Vertex Signal Processing Framework—0
Dancing under the stars: video denoising in starlight—0
A Time-Series Data Augmentation Model through Diffusion and Transformer Integration—0
LDFaceNet: Latent Diffusion-based Network for High-Fidelity Deepfake Generation—0
Fast Image Deconvolution using Hyper-Laplacian Priors—0
Fast, nonlocal and neural: a lightweight high quality solution to image denoising—0
Fast Training of Diffusion Transformer with Extreme Masking for 3D Point Clouds Generation—0
AdaIN-Switchable CycleGAN for Efficient Unsupervised Low-Dose CT Denoising—0
A Tiered Move-making Algorithm for General Non-submodular Pairwise Energies—0
A Bayesian traction force microscopy method with automated denoising in a user-friendly software package—0
DAGSM: Disentangled Avatar Generation with GS-enhanced Mesh—0
DAEs for Linear Inverse Problems: Improved Recovery with Provable Guarantees—0
A Theoretical Justification for Image Inpainting using Denoising Diffusion Probabilistic Models—0
A Geometric Approach to Color Image Regularization—0
Faster gradient descent and the efficient recovery of images—0
A task-specific deep-learning-based denoising approach for myocardial perfusion SPECT—0
D3DR: Lighting-Aware Object Insertion in Gaussian Splatting—0
AgentPolyp: Accurate Polyp Segmentation via Image Enhancement Agent—0
D²Net: A Denoising and Dereverberation Network Based on Two-branch Encoder and Dual-path Transformer—0
A Tale of Two Bases: Local-Nonlocal Regularization on Image Patches with Convolution Framelets—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