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 776–800 of 7282 papers

TitleStatusHype
Efficient and Degree-Guided Graph Generation via Discrete Diffusion ModelingCode1
Deep Speech Synthesis from MRI-Based Articulatory RepresentationsCode1
From Denoising Diffusions to Denoising Markov ModelsCode1
AKDT: Adaptive Kernel Dilation Transformer for Effective Image DenoisingCode1
Deep Universal Blind Image DenoisingCode1
Efficient and Scalable Graph Generation through Iterative Local ExpansionCode1
Full-dose Whole-body PET Synthesis from Low-dose PET Using High-efficiency Denoising Diffusion Probabilistic Model: PET Consistency ModelCode1
Efficient Diffusion Transformer with Step-wise Dynamic Attention MediatorsCode1
EEG Synthetic Data Generation Using Probabilistic Diffusion ModelsCode1
EEGdenoiseNet: A benchmark dataset for end-to-end deep learning solutions of EEG denoisingCode1
DEFT: Efficient Fine-Tuning of Diffusion Models by Learning the Generalised h-transformCode1
A Latent Space of Stochastic Diffusion Models for Zero-Shot Image Editing and GuidanceCode1
DeIL: Direct-and-Inverse CLIP for Open-World Few-Shot LearningCode1
Adaptive Cross-Layer Attention for Image RestorationCode1
Galaxy Image Deconvolution for Weak Gravitational Lensing with Unrolled Plug-and-Play ADMMCode1
CoT-BERT: Enhancing Unsupervised Sentence Representation through Chain-of-ThoughtCode1
Effective Snapshot Compressive-Spectral Imaging via Deep Denoising and Total Variation PriorsCode1
DenoiSeg: Joint Denoising and SegmentationCode1
AutoMat: Enabling Automated Crystal Structure Reconstruction from Microscopy via Agentic Tool UseCode1
EfficientDM: Efficient Quantization-Aware Fine-Tuning of Low-Bit Diffusion ModelsCode1
Adaptive Deep PnP Algorithm for Video Snapshot Compressive ImagingCode1
EMDM: Efficient Motion Diffusion Model for Fast and High-Quality Motion GenerationCode1
Enhancing MMDiT-Based Text-to-Image Models for Similar Subject GenerationCode1
EDformer: Transformer-Based Event Denoising Across Varied Noise LevelsCode1
Controlling Latent Diffusion Using Latent CLIPCode1
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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