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 3651–3675 of 7282 papers

TitleStatusHype
Evaluating Utility of Memory Efficient Medical Image Generation: A Study on Lung Nodule Segmentation—0
Evaluation of Denoising Techniques for EOG signals based on SNR Estimation—0
Evaluation of Transfer Learning for Polish with a text-to-text model—0
Evaluation of Transfer Learning for Polish with a Text-to-Text Model—0
Evaluation of Video-Based rPPG in Challenging Environments: Artifact Mitigation and Network Resilience—0
Event-Customized Image Generation—0
EventDiff: A Unified and Efficient Diffusion Model Framework for Event-based Video Frame Interpolation—0
SwiftSketch: A Diffusion Model for Image-to-Vector Sketch Generation—0
EventF2S: Asynchronous and Sparse Spiking AER Framework using Neuromorphic-Friendly Algorithm—0
Event Guided Denoising for Multilingual Relation Learning—0
Event-Guided Denoising for Multilingual Relation Learning—0
VipDiff: Towards Coherent and Diverse Video Inpainting via Training-free Denoising Diffusion Models—0
EventZoom: Learning To Denoise and Super Resolve Neuromorphic Events—0
Evolutionary Variational Optimization of Generative Models—0
Evolution Meets Diffusion: Efficient Neural Architecture Generation—0
Evolvable Conditional Diffusion—0
Evolving Deep Convolutional Neural Networks for Hyperspectral Image Denoising—0
EVRNet: Efficient Video Restoration on Edge Devices—0
EWT: Efficient Wavelet-Transformer for Single Image Denoising—0
Exact Sparse Orthogonal Dictionary Learning—0
Examining Joint Demosaicing and Denoising for Single-, Quad-, and Nona-Bayer Patterns—0
Examining the Mapping Functions of Denoising Autoencoders in Singing Voice Separation—0
Example-based super-resolution for point-cloud video—0
Explainable Artificial Intelligence driven mask design for self-supervised seismic denoising—0
Explainable Deep Learning Framework for SERS Bio-quantification—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