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 4651–4675 of 7282 papers

TitleStatusHype
Multi-Scale Adaptive Network for Single Image DenoisingCode1
Diffusion Models for Medical Anomaly DetectionCode1
Learning to Bound: A Generative Cramér-Rao BoundCode0
Compression of user generated content using denoised references—0
Measurement-conditioned Denoising Diffusion Probabilistic Model for Under-sampled Medical Image ReconstructionCode1
Weighted Mean and Median graph Filters with Attenuation Factor for Sensor Network—0
Adaptive Cross-Layer Attention for Image RestorationCode1
Geodesic Gramian Denoising Applied to the Images Contaminated With Noise Sampled From Diverse Probability Distributions—0
High Noise Immune Time-domain Inversion via Cascade Network (TICaN) for Complex Scatterers—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
Selective Residual M-Net for Real Image DenoisingCode1
NUQ: A Noise Metric for Diffusion MRI via Uncertainty Discrepancy Quantification—0
Machine learning based lens-free imaging technique for field-portable cytometryCode0
GeoBi-GNN: Geometry-aware Bi-domain Mesh Denoising via Graph Neural NetworksCode1
Beam-Shape Effects and Noise Removal from THz Time-Domain Images in Reflection Geometry in the 0.25-6 THz Range—0
Towards a unified view of unsupervised non-local methods for image denoising: the NL-Ridge approachCode0
When A Conventional Filter Meets Deep Learning: Basis Composition Learning on Image FiltersCode0
CTformer: Convolution-free Token2Token Dilated Vision Transformer for Low-dose CT DenoisingCode1
SUNet: Swin Transformer UNet for Image DenoisingCode2
Conditional Simulation Using Diffusion Schrödinger BridgesCode1
Image reconstruction algorithms in radio interferometry: from handcrafted to learned regularization denoisers—0
PRNU Emphasis: a Generalization of the Multiplicative Model—0
Computing Multiple Image Reconstructions with a Single HypernetworkCode1
Does prior knowledge in the form of multiple low-dose PET images (at different dose levels) improve standard-dose PET prediction?—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