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 4051–4075 of 7282 papers

TitleStatusHype
Speech Synthesis By Unrolling Diffusion Process using Neural Network Layers—0
All-optical image denoising using a diffractive visual processor—0
Continuous Modeling of the Denoising Process for Speech Enhancement Based on Deep Learning—0
Wavelet-based Topological Loss for Low-Light Image Denoising—0
Dual-Camera Joint Deblurring-Denoising—0
Cartoondiff: Training-free Cartoon Image Generation with Diffusion Transformer Models—0
Large Intestine 3D Shape Refinement Using Point Diffusion Models for Digital Phantom Generation—0
Hyperspectral Image Denoising via Self-Modulating Convolutional Neural NetworksCode0
A Unified View Between Tensor Hypergraph Neural Networks And Signal Denoising—0
Complexity Scaling for Speech Denoising—0
Masked Diffusion with Task-awareness for Procedure Planning in Instructional VideosCode0
Deep Nonparametric Convexified Filtering for Computational Photography, Image Synthesis and Adversarial Defense—0
Introducing Shape Prior Module in Diffusion Model for Medical Image Segmentation—0
CleanUNet 2: A Hybrid Speech Denoising Model on Waveform and Spectrogram—0
Diff-Privacy: Diffusion-based Face Privacy Protection—0
Predicting the Radiation Field of Molecular Clouds using Denoising Diffusion Probabilistic Models—0
Restoring Snow-Degraded Single Images With Wavelet in Vision TransformerCode0
Discrete Denoising Diffusion Approach to Integer FactorizationCode0
Prefix-diffusion: A Lightweight Diffusion Model for Diverse Image Captioning—0
Effective Real Image Editing with Accelerated Iterative Diffusion Inversion—0
DeNoising-MOT: Towards Multiple Object Tracking with Severe Occlusions—0
3D Denoisers are Good 2D Teachers: Molecular Pretraining via Denoising and Cross-Modal Distillation—0
From Text to Mask: Localizing Entities Using the Attention of Text-to-Image Diffusion ModelsCode0
MS-UNet-v2: Adaptive Denoising Method and Training Strategy for Medical Image Segmentation with Small Training Data—0
Data-Adaptive Graph Framelets with Generalized Vanishing Moments for Graph Signal ProcessingCode0
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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