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 3401–3425 of 7282 papers

TitleStatusHype
Probabilistic-based Feature Embedding of 4-D Light Fields for Compressive Imaging and DenoisingCode1
Generative Proxemics: A Prior for 3D Social Interaction from ImagesCode1
Towards Faster Non-Asymptotic Convergence for Diffusion-Based Generative Models—0
DiffAug: A Diffuse-and-Denoise Augmentation for Training Robust Classifiers—0
RIDNet Assisted cGAN Based Channel Estimation for One-Bit ADC mmWave MIMO Systems—0
Fast Training of Diffusion Models with Masked TransformersCode2
On the Robustness of Latent Diffusion ModelsCode1
PoetryDiffusion: Towards Joint Semantic and Metrical Manipulation in Poetry GenerationCode0
Diffusion in Diffusion: Cyclic One-Way Diffusion for Text-Vision-Conditioned GenerationCode1
Paste, Inpaint and Harmonize via Denoising: Subject-Driven Image Editing with Pre-Trained Diffusion Model—0
3D molecule generation by denoising voxel gridsCode1
VillanDiffusion: A Unified Backdoor Attack Framework for Diffusion ModelsCode1
Deep Ultrasound Denoising Using Diffusion Probabilistic Models—0
HiddenSinger: High-Quality Singing Voice Synthesis via Neural Audio Codec and Latent Diffusion Models—0
Generative Plug and Play: Posterior Sampling for Inverse ProblemsCode0
Deep denoising autoencoder-based non-invasive blood flow detection for arteriovenous fistula—0
Diffusion Models for Black-Box OptimizationCode1
A Deep Unrolling Model with Hybrid Optimization Structure for Hyperspectral Image Deconvolution—0
Boosting Fast and High-Quality Speech Synthesis with Linear Diffusion—0
One-shot Learning for Channel Estimation in Massive MIMO Systems—0
Motion-DVAE: Unsupervised learning for fast human motion denoising—0
Beyond Surface Statistics: Scene Representations in a Latent Diffusion ModelCode1
Protein Discovery with Discrete Walk-Jump SamplingCode1
Joint Channel Estimation and Feedback with Masked Token Transformers in Massive MIMO Systems—0
ADDP: Learning General Representations for Image Recognition and Generation with Alternating Denoising Diffusion ProcessCode1
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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