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 4376–4400 of 7282 papers

TitleStatusHype
Position Bias Estimation with Item Embedding for Sparse Dataset—0
Multi-Object Self-Supervised Depth DenoisingCode0
SwinIA: Self-Supervised Blind-Spot Image Denoising without Convolutions—0
Stochastic Texture Filtering—0
Multi-Granularity Denoising and Bidirectional Alignment for Weakly Supervised Semantic SegmentationCode0
Real-World Denoising via Diffusion Model—0
Autoencoder-based prediction of ICU clinical codesCode0
Multi-Scale Energy (MuSE) plug and play framework for inverse problems—0
DiffBFR: Bootstrapping Diffusion Model Towards Blind Face Restoration—0
Diffusion-NAT: Self-Prompting Discrete Diffusion for Non-Autoregressive Text Generation—0
Degradation-Noise-Aware Deep Unfolding Transformer for Hyperspectral Image Denoising—0
Towards Prompt-robust Face Privacy Protection via Adversarial Decoupling Augmentation Framework—0
Denoising-Contrastive Alignment for Continuous Sign Language Recognition—0
A multimodal dynamical variational autoencoder for audiovisual speech representation learningCode0
Contrastive Learning for Low-light Raw Denoising—0
Guided Image Synthesis via Initial Image Editing in Diffusion ModelCode0
Steered Mixture-of-Experts Autoencoder Design for Real-Time Image Modelling and Denoising—0
LayoutDM: Transformer-based Diffusion Model for Layout Generation—0
Multimodal-driven Talking Face Generation via a Unified Diffusion-based Generator—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
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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