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 5176–5200 of 7282 papers

TitleStatusHype
Geometry-aware Two-scale PIFu Representation for Human Reconstruction—0
Recovering Hölder smooth functions from noisy modulo samplesCode0
Wide-Sense Stationarity in Generalized Graph Signal Processing—0
Three-dimensional spike localization and improved motion correction for Neuropixels recordings—0
Graph Differentiable Architecture Search with Structure Learning—0
It Has Potential: Gradient-Driven Denoisers for Convergent Solutions to Inverse Problems—0
Noise2Score: Tweedie’s Approach to Self-Supervised Image Denoising without Clean Images—0
Stochastic Solutions for Linear Inverse Problems using the Prior Implicit in a Denoiser—0
Total-Body Low-Dose CT Image Denoising using Prior Knowledge Transfer Technique with Contrastive Regularization Mechanism—0
RADU: Ray-Aligned Depth Update Convolutions for ToF Data DenoisingCode0
Trust the Critics: Generatorless and Multipurpose WGANs with Initial Convergence GuaranteesCode0
Image denoising by Super Neurons: Why go deep?—0
Joint inference and input optimization in equilibrium networksCode0
Supervised Neural Discrete Universal Denoiser for Adaptive Denoising—0
Deep Point Cloud Reconstruction—0
Bilevel learning of l1-regularizers with closed-form gradients(BLORC)—0
Vehicular Visible Light Communications Noise Analysis and Autoencoder Based Denoising—0
Switching Independent Vector Analysis and Its Extension to Blind and Spatially Guided Convolutional Beamforming Algorithms—0
Image enhancement in acoustic-resolution photoacoustic microscopy enabled by a novel directional algorithm—0
Enhanced countering adversarial attacks via input denoising and feature restoringCode0
Deep neural networks-based denoising models for CT imaging and their efficacy—0
CLMB: deep contrastive learning for robust metagenomic binningCode0
LiDAR-Aided Mobile Blockage Prediction in Real-World Millimeter Wave Systems—0
Discriminative Dictionary Learning based on Statistical Methods—0
Fast and Light-Weight Network for Single Frame Structured Illumination Microscopy Super-Resolution—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