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 1626–1650 of 7282 papers

TitleStatusHype
Deep learning-based denoising for fast time-resolved flame emission spectroscopy in high-pressure combustion environmentCode1
MANet: Improving Video Denoising with a Multi-Alignment NetworkCode1
Deep Reparametrization of Multi-Frame Super-Resolution and DenoisingCode1
Lung-DDPM: Semantic Layout-guided Diffusion Models for Thoracic CT Image SynthesisCode1
IDR: Self-Supervised Image Denoising via Iterative Data RefinementCode1
Accelerated MRI with Un-trained Neural NetworksCode1
A deep network for sinogram and CT image reconstructionCode1
Deep Recurrent Neural Networks for ECG Signal DenoisingCode1
SODA: Bottleneck Diffusion Models for Representation LearningCode1
Aligning Few-Step Diffusion Models with Dense Reward Difference LearningCode1
Image Denoising and the Generative Accumulation of PhotonsCode1
A Variational Perspective on Solving Inverse Problems with Diffusion ModelsCode1
Image Denoising Using Green Channel PriorCode1
Adaptive Diffusion Terrain Generator for Autonomous Uneven Terrain NavigationCode1
M2DF: Multi-grained Multi-curriculum Denoising Framework for Multimodal Aspect-based Sentiment AnalysisCode1
Accurate Image Restoration with Attention Retractable TransformerCode1
Spach Transformer: Spatial and Channel-wise Transformer Based on Local and Global Self-attentions for PET Image DenoisingCode1
Aligning Generative Denoising with Discriminative Objectives Unleashes Diffusion for Visual PerceptionCode1
Image Restoration by Denoising Diffusion Models with Iteratively Preconditioned GuidanceCode1
Spatial-Adaptive Network for Single Image DenoisingCode1
Formulating Event-based Image Reconstruction as a Linear Inverse Problem with Deep Regularization using Optical FlowCode1
Image Restoration via Frequency SelectionCode1
Images as Weight Matrices: Sequential Image Generation Through Synaptic Learning RulesCode1
Speckle2Void: Deep Self-Supervised SAR Despeckling with Blind-Spot Convolutional Neural NetworksCode1
Can LLMs be Good Graph Judge for Knowledge Graph Construction?Code1
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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