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 651–675 of 7282 papers

TitleStatusHype
Crystal Structure Prediction by Joint Equivariant DiffusionCode1
CTformer: Convolution-free Token2Token Dilated Vision Transformer for Low-dose CT DenoisingCode1
Ab-initio Contrast Estimation and Denoising of Cryo-EM ImagesCode1
CT-Mamba: A Hybrid Convolutional State Space Model for Low-Dose CT DenoisingCode1
DP-IQA: Utilizing Diffusion Prior for Blind Image Quality Assessment in the WildCode1
DPMesh: Exploiting Diffusion Prior for Occluded Human Mesh RecoveryCode1
Cross-Level Distillation and Feature Denoising for Cross-Domain Few-Shot ClassificationCode1
CrackSegDiff: Diffusion Probability Model-based Multi-modal Crack SegmentationCode1
DPCSpell: A Transformer-based Detector-Purificator-Corrector Framework for Spelling Error Correction of Bangla and Resource Scarce Indic LanguagesCode1
DPM-OT: A New Diffusion Probabilistic Model Based on Optimal TransportCode1
COVE: Unleashing the Diffusion Feature Correspondence for Consistent Video EditingCode1
Customized Generation Reimagined: Fidelity and Editability HarmonizedCode1
CPT: A Pre-Trained Unbalanced Transformer for Both Chinese Language Understanding and GenerationCode1
DoseDiff: Distance-aware Diffusion Model for Dose Prediction in RadiotherapyCode1
DogLayout: Denoising Diffusion GAN for Discrete and Continuous Layout GenerationCode1
Domain Generalization for Object Recognition with Multi-task AutoencodersCode1
CoreDiff: Contextual Error-Modulated Generalized Diffusion Model for Low-Dose CT Denoising and GeneralizationCode1
Does Localization Inform Editing? Surprising Differences in Causality-Based Localization vs. Knowledge Editing in Language ModelsCode1
DomainRAG: A Chinese Benchmark for Evaluating Domain-specific Retrieval-Augmented GenerationCode1
DnSwin: Toward Real-World Denoising via Continuous Wavelet Sliding-TransformerCode1
A generic diffusion-based approach for 3D human pose prediction in the wildCode1
DNTextSpotter: Arbitrary-Shaped Scene Text Spotting via Improved Denoising TrainingCode1
Convolutional Proximal Neural Networks and Plug-and-Play AlgorithmsCode1
CoT-BERT: Enhancing Unsupervised Sentence Representation through Chain-of-ThoughtCode1
Don't Pay Attention to the Noise: Learning Self-supervised Representations of Light Curves with a Denoising Time Series TransformerCode1
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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