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 4626–4650 of 7282 papers

TitleStatusHype
LaSe-E2V: Towards Language-guided Semantic-Aware Event-to-Video Reconstruction—0
Diffusion Transformer-based Universal Dose Denoising for Pencil Beam Scanning Proton Therapy—0
Time Series Diffusion Method: A Denoising Diffusion Probabilistic Model for Vibration Signal Generation—0
Latent Diffusion Model Based Denoising Receiver for 6G Semantic Communication: From Stochastic Differential Theory to Application—0
Time-series image denoising of pressure-sensitive paint data by projected multivariate singular spectrum analysis—0
Latent Diffusion Model for Generating Ensembles of Climate Simulations—0
Latent diffusion models for parameterization and data assimilation of facies-based geomodels—0
Time Series Language Model for Descriptive Caption Generation—0
Latent Painter—0
Latent Space Characterization of Autoencoder Variants—0
A deep learning framework for segmentation of retinal layers from OCT images—0
LatentWarp: Consistent Diffusion Latents for Zero-Shot Video-to-Video Translation—0
Latent Wavelet Diffusion: Enabling 4K Image Synthesis for Free—0
Latent Weight Diffusion: Generating reactive policies instead of trajectories—0
LatexBlend: Scaling Multi-concept Customized Generation with Latent Textual Blending—0
Lattice Fusion Networks for Image Denoising—0
Layer- and Timestep-Adaptive Differentiable Token Compression Ratios for Efficient Diffusion Transformers—0
Layer Depth Denoising and Completion for Structured-Light RGB-D Cameras—0
Layered Motion Fusion: Lifting Motion Segmentation to 3D in Egocentric Videos—0
LayoutDM: Transformer-based Diffusion Model for Layout Generation—0
LayoutFlow: Flow Matching for Layout Generation—0
Layout Sequence Prediction From Noisy Mobile Modality—0
LazyDiT: Lazy Learning for the Acceleration of Diffusion Transformers—0
LBF:Learnable Bilateral Filter For Point Cloud Denoising—0
LC4SV: A Denoising Framework Learning to Compensate for Unseen Speaker Verification Models—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