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 5051–5075 of 7282 papers

TitleStatusHype
Adversarial Defense via Image Denoising with Chaotic Encryption—0
RoVISQ: Reduction of Video Service Quality via Adversarial Attacks on Deep Learning-based Video Compression—0
Neural Compression-Based Feature Learning for Video Restoration—0
DU-VLG: Unifying Vision-and-Language Generation via Dual Sequence-to-Sequence Pre-training—0
Graph filtering over expanding graphs—0
A Noise-level-aware Framework for PET Image Denoising—0
Joint Time-Vertex Fractional Fourier Transform—0
Time-series image denoising of pressure-sensitive paint data by projected multivariate singular spectrum analysis—0
FB-MSTCN: A Full-Band Single-Channel Speech Enhancement Method Based on Multi-Scale Temporal Convolutional Network—0
Denoising and feature extraction in photoemission spectra with variational auto-encoder neural networks—0
Change Detection from Synthetic Aperture Radar Images via Dual Path Denoising Network—0
Dual reparametrized Variational Generative Model for Time-Series Forecasting—0
Manifold Modeling in Quotient Space: Learning An Invariant Mapping with Decodability of Image Patches—0
Dynamic Dual-Output Diffusion Models—0
Learning to Bound: A Generative Cramér-Rao BoundCode0
Compression of user generated content using denoised references—0
Weighted Mean and Median graph Filters with Attenuation Factor for Sensor Network—0
Geodesic Gramian Denoising Applied to the Images Contaminated With Noise Sampled From Diverse Probability Distributions—0
NUQ: A Noise Metric for Diffusion MRI via Uncertainty Discrepancy Quantification—0
Investigating the limited performance of a deep-learning-based SPECT denoising approach: An observer-study-based characterization—0
Robust Segmentation of Brain MRI in the Wild with Hierarchical CNNs and no RetrainingCode0
High Noise Immune Time-domain Inversion via Cascade Network (TICaN) for Complex Scatterers—0
Machine learning based lens-free imaging technique for field-portable cytometryCode0
Towards a unified view of unsupervised non-local methods for image denoising: the NL-Ridge approachCode0
Beam-Shape Effects and Noise Removal from THz Time-Domain Images in Reflection Geometry in the 0.25-6 THz Range—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