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 6401–6425 of 7282 papers

TitleStatusHype
DiffGAN-F2S: Symmetric and Efficient Denoising Diffusion GANs for Structural Connectivity Prediction from Brain fMRI—0
DiffGANPaint: Fast Inpainting Using Denoising Diffusion GANs—0
DiffGAN-TTS: High-Fidelity and Efficient Text-to-Speech with Denoising Diffusion GANs—0
DiffGAP: A Lightweight Diffusion Module in Contrastive Space for Bridging Cross-Model Gap—0
DiffGaze: A Diffusion Model for Continuous Gaze Sequence Generation on 360° Images—0
AMOSL: Adaptive Modality-wise Structure Learning in Multi-view Graph Neural Networks For Enhanced Unified Representation—0
DiffHand: End-to-End Hand Mesh Reconstruction via Diffusion Models—0
DiffHuman: Probabilistic Photorealistic 3D Reconstruction of Humans—0
DiffI2I: Efficient Diffusion Model for Image-to-Image Translation—0
DiffImp: Efficient Diffusion Model for Probabilistic Time Series Imputation with Bidirectional Mamba Backbone—0
Matrix Completion using Kronecker Product Approximation—0
StableMoFusion: Towards Robust and Efficient Diffusion-based Motion Generation Framework—0
Amortised MAP Inference for Image Super-resolution—0
A Modified PINN Approach for Identifiable Compartmental Models in Epidemiology with Applications to COVID-19—0
A Missing Information Loss function for implicit feedback datasets—0
DiffKAN-Inpainting: KAN-based Diffusion model for brain tumor inpainting—0
DiffLLE: Diffusion-guided Domain Calibration for Unsupervised Low-light Image Enhancement—0
A method of limiting performance loss of CNNs in noisy environments—0
DiffMOD: Progressive Diffusion Point Denoising for Moving Object Detection in Remote Sensing—0
DiffNAS: Bootstrapping Diffusion Models by Prompting for Better Architectures—0
VideoGuide: Improving Video Diffusion Models without Training Through a Teacher's Guide—0
VideoMerge: Towards Training-free Long Video Generation—0
Stable Video Portraits—0
Diff-PCR: Diffusion-Based Correspondence Searching in Doubly Stochastic Matrix Space for Point Cloud Registration—0
Stacked Autoencoder Based Deep Random Vector Functional Link Neural Network for Classification—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