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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 4251–4275 of 7282 papers

TitleStatusHype
Inertial Navigation Meets Deep Learning: A Survey of Current Trends and Future Directions—0
Directional diffusion models for graph representation learning—0
HSR-Diff:Hyperspectral Image Super-Resolution via Conditional Diffusion Models—0
Resilient Sparse Array Radar with the Aid of Deep Learning—0
EMoG: Synthesizing Emotive Co-speech 3D Gesture with Diffusion Model—0
Conditional expectation using compactification operators—0
Weighted structure tensor total variation for image denoising—0
Efficient HDR Reconstruction from Real-World Raw Images—0
CANDID: Correspondence AligNment for Deep-burst Image Denoising—0
Drag-guided diffusion models for vehicle image generation—0
Structural Restricted Boltzmann Machine for image denoising and classification—0
RIDNet Assisted cGAN Based Channel Estimation for One-Bit ADC mmWave MIMO Systems—0
Diff-TTSG: Denoising probabilistic integrated speech and gesture synthesis—0
Towards Faster Non-Asymptotic Convergence for Diffusion-Based Generative Models—0
DiffAug: A Diffuse-and-Denoise Augmentation for Training Robust Classifiers—0
R2-Diff: Denoising by diffusion as a refinement of retrieved motion for image-based motion prediction—0
PoetryDiffusion: Towards Joint Semantic and Metrical Manipulation in Poetry GenerationCode0
Paste, Inpaint and Harmonize via Denoising: Subject-Driven Image Editing with Pre-Trained Diffusion Model—0
Deep Ultrasound Denoising Using Diffusion Probabilistic Models—0
Deep denoising autoencoder-based non-invasive blood flow detection for arteriovenous fistula—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
A Deep Unrolling Model with Hybrid Optimization Structure for Hyperspectral Image Deconvolution—0
One-shot Learning for Channel Estimation in Massive MIMO Systems—0
Motion-DVAE: Unsupervised learning for fast human motion 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