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 6601–6650 of 7282 papers

TitleStatusHype
A Lesson in Splats: Teacher-Guided Diffusion for 3D Gaussian Splats Generation with 2D Supervision—0
Dig2DIG: Dig into Diffusion Information Gains for Image Fusion—0
Digging Deeper into CRNN Model in Chinese Text Images Recognition—0
3D U-NetR: Low Dose Computed Tomography Reconstruction via Deep Learning and 3 Dimensional Convolutions—0
A Lennard-Jones Layer for Distribution Normalization—0
Dilated Deep Residual Network for Image Denoising—0
Dimension-Free Convergence of Diffusion Models for Approximate Gaussian Mixtures—0
DimensionX: Create Any 3D and 4D Scenes from a Single Image with Controllable Video Diffusion—0
Diminishing Stereotype Bias in Image Generation Model using Reinforcemenlent Learning Feedback—0
DINF: Dynamic Instance Noise Filter for Occluded Pedestrian Detection—0
DI-PCG: Diffusion-based Efficient Inverse Procedural Content Generation for High-quality 3D Asset Creation—0
DiP-GO: A Diffusion Pruner via Few-step Gradient Optimization—0
Directional Bilateral Filters—0
Directional diffusion models for graph representation learning—0
Directly Denoising Diffusion Models—0
Unsupervised parameter selection for denoising with the elastic net—0
Direct Reconstruction of Linear Parametric Images from Dynamic PET Using Nonlocal Deep Image Prior—0
Direct Unsupervised Denoising—0
A Learning-from-noise Dilated Wide Activation Network for denoising Arterial Spin Labeling (ASL) Perfusion Images—0
DISCO: Efficient Diffusion Solver for Large-Scale Combinatorial Optimization Problems—0
3D seismic data denoising using two-dimensional sparse coding scheme—0
Streaming Networks: Enable A Robust Classification of Noise-Corrupted Images—0
Discrete Copula Diffusion—0
Discrete Denoising Flows—0
A Large Language Model and Denoising Diffusion Framework for Targeted Design of Microstructures with Commands in Natural Language—0
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