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 426–450 of 7282 papers

TitleStatusHype
On the Generalization of BasicVSR++ to Video Deblurring and DenoisingCode2
iSDF: Real-Time Neural Signed Distance Fields for Robot PerceptionCode2
Practical Blind Image Denoising via Swin-Conv-UNet and Data SynthesisCode2
SUNet: Swin Transformer UNet for Image DenoisingCode2
Speech Denoising in the Waveform Domain with Self-AttentionCode2
Denoising Diffusion Restoration ModelsCode2
SPIRAL: Self-supervised Perturbation-Invariant Representation Learning for Speech Pre-TrainingCode2
PromptBERT: Improving BERT Sentence Embeddings with PromptsCode2
Improving Image Restoration by Revisiting Global Information AggregationCode2
ExT5: Towards Extreme Multi-Task Scaling for Transfer LearningCode2
Palette: Image-to-Image Diffusion ModelsCode2
QuantumNAT: Quantum Noise-Aware Training with Noise Injection, Quantization and NormalizationCode2
SDEdit: Guided Image Synthesis and Editing with Stochastic Differential EquationsCode2
Structured Denoising Diffusion Models in Discrete State-SpacesCode2
SeaD: End-to-end Text-to-SQL Generation with Schema-aware DenoisingCode2
Liquid Warping GAN with Attention: A Unified Framework for Human Image SynthesisCode2
Denoising Diffusion Implicit ModelsCode2
Denoising Diffusion Probabilistic ModelsCode2
LangMamba: A Language-driven Mamba Framework for Low-dose CT Denoising with Vision-language ModelsCode1
ScoreAdv: Score-based Targeted Generation of Natural Adversarial Examples via Diffusion ModelsCode1
Elucidated Rolling Diffusion Models for Probabilistic Weather ForecastingCode1
Visual-Instructed Degradation Diffusion for All-in-One Image RestorationCode1
DiffO: Single-step Diffusion for Image Compression at Ultra-Low BitratesCode1
Noise Conditional Variational Score DistillationCode1
StableMTL: Repurposing Latent Diffusion Models for Multi-Task Learning from Partially Annotated Synthetic DatasetsCode1
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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