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 1101–1125 of 7282 papers

TitleStatusHype
Aerial Height Prediction and Refinement Neural Networks with Semantic and Geometric GuidanceCode1
Heterogeneous window transformer for image denoisingCode1
DAS-N2N: Machine learning Distributed Acoustic Sensing (DAS) signal denoising without clean dataCode1
HarmoniCa: Harmonizing Training and Inference for Better Feature Caching in Diffusion Transformer AccelerationCode1
Data Augmentation for Supervised Graph Outlier Detection via Latent Diffusion ModelsCode1
Harnessing the Spatial-Temporal Attention of Diffusion Models for High-Fidelity Text-to-Image SynthesisCode1
D4AM: A General Denoising Framework for Downstream Acoustic ModelsCode1
D3RM: A Discrete Denoising Diffusion Refinement Model for Piano TranscriptionCode1
D4Explainer: In-Distribution GNN Explanations via Discrete Denoising DiffusionCode1
D3A-TS: Denoising-Driven Data Augmentation in Time SeriesCode1
A Comparison of Image Denoising MethodsCode1
D^4-VTON: Dynamic Semantics Disentangling for Differential Diffusion based Virtual Try-OnCode1
Guaranteed Tensor Recovery Fused Low-rankness and SmoothnessCode1
D2HNet: Joint Denoising and Deblurring with Hierarchical Network for Robust Night Image RestorationCode1
Comparison of Image Quality Models for Optimization of Image Processing SystemsCode1
DAEMA: Denoising Autoencoder with Mask AttentionCode1
An Organism Starts with a Single Pix-Cell: A Neural Cellular Diffusion for High-Resolution Image SynthesisCode1
AdjointDPM: Adjoint Sensitivity Method for Gradient Backpropagation of Diffusion Probabilistic ModelsCode1
Dynamic Addition of Noise in a Diffusion Model for Anomaly DetectionCode1
G-SimCLR : Self-Supervised Contrastive Learning with Guided Projection via Pseudo LabellingCode1
D2C: Diffusion-Denoising Models for Few-shot Conditional GenerationCode1
Cyclic Test-Time Adaptation on Monocular Video for 3D Human Mesh ReconstructionCode1
GRDN:Grouped Residual Dense Network for Real Image Denoising and GAN-based Real-world Noise ModelingCode1
D^2-DPM: Dual Denoising for Quantized Diffusion Probabilistic ModelsCode1
CycleISP: Real Image Restoration via Improved Data SynthesisCode1
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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