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

Showing 12511275 of 7282 papers

TitleStatusHype
Weak-signal extraction enabled by deep-neural-network denoising of diffraction dataCode1
Deep Plug-and-Play Prior for Hyperspectral Image RestorationCode1
4DenoiseNet: Adverse Weather Denoising from Adjacent Point CloudsCode1
Learning to Generate Realistic LiDAR Point CloudsCode1
Spach Transformer: Spatial and Channel-wise Transformer Based on Local and Global Self-attentions for PET Image DenoisingCode1
MODNet: Multi-offset Point Cloud Denoising Network Customized for Multi-scale PatchesCode1
Frido: Feature Pyramid Diffusion for Complex Scene Image SynthesisCode1
Your ViT is Secretly a Hybrid Discriminative-Generative Diffusion ModelCode1
Learning Semantic Correspondence with Sparse AnnotationsCode1
Learning Degradation Representations for Image DeblurringCode1
High-Frequency Space Diffusion Models for Accelerated MRICode1
Rethinking Degradation: Radiograph Super-Resolution via AID-SRGANCode1
Exploring Generative Neural Temporal Point ProcessCode1
Deep learning-based denoising for fast time-resolved flame emission spectroscopy in high-pressure combustion environmentCode1
DnSwin: Toward Real-World Denoising via Continuous Wavelet Sliding-TransformerCode1
Optimizing Image Compression via Joint Learning with DenoisingCode1
Deep Audio Waveform PriorCode1
Deep Semantic Statistics Matching (D2SM) Denoising NetworkCode1
Threat Model-Agnostic Adversarial Defense using Diffusion ModelsCode1
Real-time Streaming Video Denoising with Bidirectional BuffersCode1
Learnability Enhancement for Low-light Raw Denoising: Where Paired Real Data Meets Noise ModelingCode1
D2HNet: Joint Denoising and Deblurring with Hierarchical Network for Robust Night Image RestorationCode1
Patch-wise Deep Metric Learning for Unsupervised Low-Dose CT DenoisingCode1
Don't Pay Attention to the Noise: Learning Self-supervised Representations of Light Curves with a Denoising Time Series TransformerCode1
Deep Parametric 3D Filters for Joint Video Denoising and Illumination Enhancement in Video Super ResolutionCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1SINDyPSNR81Unverified
2Pixel-shuffling DownsamplingPSNR38.4Unverified
3TWSCPSNR37.93Unverified
4CBDNet(Syn)PSNR37.57Unverified
5MCWNNMPSNR37.38Unverified
6Han et alPSNR35.95Unverified
7FFDNetPSNR34.4Unverified
8TNRDPSNR33.65Unverified
9CDnCNN-BPSNR32.43Unverified
10NLRNPSNR30.8Unverified
#ModelMetricClaimedVerifiedStatus
1DRUnet_Poisson_0.01Average PSNR (dB)33.92Unverified
#ModelMetricClaimedVerifiedStatus
1DRANetAverage PSNR39.64Unverified
#ModelMetricClaimedVerifiedStatus
1PCNN+RL+HMEAverage84.61Unverified