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 3601–3625 of 7282 papers

TitleStatusHype
A Variational Perspective on Solving Inverse Problems with Diffusion ModelsCode1
Dual Residual Attention Network for Image DenoisingCode1
Degradation-Noise-Aware Deep Unfolding Transformer for Hyperspectral Image Denoising—0
Towards Prompt-robust Face Privacy Protection via Adversarial Decoupling Augmentation Framework—0
Efficient and Degree-Guided Graph Generation via Discrete Diffusion ModelingCode1
DocDiff: Document Enhancement via Residual Diffusion ModelsCode2
SST-ReversibleNet: Reversible-prior-based Spectral-Spatial Transformer for Efficient Hyperspectral Image ReconstructionCode1
Diffusion-NAT: Self-Prompting Discrete Diffusion for Non-Autoregressive Text Generation—0
2D medical image synthesis using transformer-based denoising diffusion probabilistic modelCode1
Steered Mixture-of-Experts Autoencoder Design for Real-Time Image Modelling and Denoising—0
A multimodal dynamical variational autoencoder for audiovisual speech representation learningCode0
Guided Image Synthesis via Initial Image Editing in Diffusion ModelCode0
Contrastive Learning for Low-light Raw Denoising—0
Denoising-Contrastive Alignment for Continuous Sign Language Recognition—0
DisenBooth: Identity-Preserving Disentangled Tuning for Subject-Driven Text-to-Image GenerationCode1
Multimodal-driven Talking Face Generation via a Unified Diffusion-based Generator—0
LayoutDM: Transformer-based Diffusion Model for Layout Generation—0
Denoising Multi-modal Sequential Recommenders with Contrastive Learning—0
Solving Inverse Problems with Score-Based Generative Priors learned from Noisy Data—0
RViDeformer: Efficient Raw Video Denoising Transformer with a Larger Benchmark Dataset—0
Joint tone mapping and denoising of thermal infrared images via multi-scale Retinex and multi-task learning—0
Sparsity-Aware Optimal Transport for Unsupervised Restoration Learning—0
Unified Noise-aware Network for Low-count PET Denoising—0
Cycle-guided Denoising Diffusion Probability Model for 3D Cross-modality MRI Synthesis—0
Knowledge-refined Denoising Network for Robust RecommendationCode1
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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