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

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Showing 6626–6650 of 7282 papers

TitleStatusHype
A Label-Free and Non-Monotonic Metric for Evaluating Denoising in Event Cameras—0
Streamlining Image Editing with Layered Diffusion Brushes—0
Discrete Potts Model for Generating Superpixels on Noisy Images—0
Discrete to Continuous: Generating Smooth Transition Poses from Sign Language Observation—0
Discrete to Continuous: Generating Smooth Transition Poses from Sign Language Observations—0
Discrete vs. Continuous Trade-offs for Generative Models—0
Discriminative Dictionary Learning based on Statistical Methods—0
Discriminative Optimization: Theory and Applications to Computer Vision Problems—0
Discriminative protein sequence modelling with Latent Space Diffusion—0
Discriminative training of conditional random fields with probably submodular constraints—0
Discriminative Transfer Learning for General Image Restoration—0
Stream Query Denoising for Vectorized HD Map Construction—0
Disentangle and denoise: Tackling context misalignment for video moment retrieval—0
Disentangling Disentangled Representations: Towards Improved Latent Units via Diffusion Models—0
Strong denoising of financial time-series—0
Image Statistics Predict the Sensitivity of Perceptual Quality Metrics—0
Dissipative residual layers for unsupervised implicit parameterization of data manifolds—0
DISTA: Denoising Spiking Transformer with intrinsic plasticity and spatiotemporal attention—0
Structural Restricted Boltzmann Machine for image denoising and classification—0
Distill and Calibrate: Denoising Inconsistent Labeling Instances for Chinese Named Entity Recognition—0
Distilling Knowledge from Deep Networks with Applications to Healthcare Domain—0
Distilling ODE Solvers of Diffusion Models into Smaller Steps—0
Distilling Semantic Priors from SAM to Efficient Image Restoration Models—0
Distinguished Quantized Guidance for Diffusion-based Sequence Recommendation—0
Distraction is All You Need: Memory-Efficient Image Immunization against Diffusion-Based Image Editing—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