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

Showing 25512575 of 7282 papers

TitleStatusHype
Anatomical Priors for Image Segmentation via Post-Processing with Denoising AutoencodersCode0
Heteroskedastic PCA: Algorithm, Optimality, and ApplicationsCode0
HPPP: Halpern-type Preconditioned Proximal Point Algorithms and Applications to Image RestorationCode0
Image Blind Denoising Using Dual Convolutional Neural Network with Skip ConnectionCode0
Guided and Variance-Corrected Fusion with One-shot Style Alignment for Large-Content Image GenerationCode0
Learning normalized image densities via dual score matchingCode0
Eigen-CNN: Eigenimages Plus Eigennoise Level Maps Guided Network for Hyperspectral Image DenoisingCode0
Learning parametric dictionaries for graph signalsCode0
Guided Image Synthesis via Initial Image Editing in Diffusion ModelCode0
Ground Truth Free Denoising by Optimal TransportCode0
Graph topology inference benchmarks for machine learningCode0
Grids Often Outperform Implicit Neural RepresentationsCode0
A note on the evaluation of generative modelsCode0
Learning to Bound: A Generative Cramér-Rao BoundCode0
Discrete Object Generation with Reversible Inductive ConstructionCode0
ELMformer: Efficient Raw Image Restoration with a Locally Multiplicative TransformerCode0
CoDiCast: Conditional Diffusion Model for Global Weather Prediction with Uncertainty QuantificationCode0
Discrete Denoising Diffusion Approach to Integer FactorizationCode0
Graph Denoising with Framelet RegularizerCode0
Graph Signal Recovery Using Restricted Boltzmann MachinesCode0
Haar-Laplacian for directed graphsCode0
Going beyond Compositions, DDPMs Can Produce Zero-Shot InterpolationsCode0
Gold: A Global and Local-aware Denoising Framework for Commonsense Knowledge Graph Noise DetectionCode0
Dirty Pixels: Towards End-to-End Image Processing and PerceptionCode0
CoDe: Blockwise Control for Denoising Diffusion ModelsCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1SINDyPSNR81Unverified
2Pixel-shuffling DownsamplingPSNR38.4Unverified
3TWSCPSNR37.93Unverified
4CBDNet(Syn)PSNR37.57Unverified
5MCWNNMPSNR37.38Unverified
6Han et alPSNR35.95Unverified
7FFDNetPSNR34.4Unverified
8TNRDPSNR33.65Unverified
9CDnCNN-BPSNR32.43Unverified
10NLRNPSNR30.8Unverified
#ModelMetricClaimedVerifiedStatus
1DRUnet_Poisson_0.01Average PSNR (dB)33.92Unverified
#ModelMetricClaimedVerifiedStatus
1DRANetAverage PSNR39.64Unverified
#ModelMetricClaimedVerifiedStatus
1PCNN+RL+HMEAverage84.61Unverified