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 5926–5950 of 7282 papers

TitleStatusHype
Sparse High-Dimensional Linear Regression. Algorithmic Barriers and a Local Search Algorithm—0
CT Image Denoising with Perceptive Deep Neural Networks—0
CTLformer: A Hybrid Denoising Model Combining Convolutional Layers and Self-Attention for Enhanced CT Image Reconstruction—0
Ctrl-Z Sampling: Diffusion Sampling with Controlled Random Zigzag Explorations—0
CT Synthesis with Conditional Diffusion Models for Abdominal Lymph Node Segmentation—0
Parallel Diffusion Model-based Sparse-view Cone-beam Breast CT—0
CUNI Submission to MT4All Shared Task—0
Analyzing Neural Network-Based Generative Diffusion Models through Convex Optimization—0
Sparse Measurement Medical CT Reconstruction using Multi-Fused Block Matching Denoising Priors—0
SparseMeXT Unlocking the Potential of Sparse Representations for HD Map Construction—0
Sparse Mixture-of-Experts for Non-Uniform Noise Reduction in MRI Images—0
Sparse Models for Machine Learning—0
Sparse Nonnegative Tensor Factorization and Completion with Noisy Observations—0
CWGAN-GP Augmented CAE for Jamming Detection in 5G-NR in Non-IID Datasets—0
Cycle3D: High-quality and Consistent Image-to-3D Generation via Generation-Reconstruction Cycle—0
Self-Consistent Nested Diffusion Bridge for Accelerated MRI Reconstruction—0
Cycle-Constrained Adversarial Denoising Convolutional Network for PET Image Denoising: Multi-Dimensional Validation on Large Datasets with Reader Study and Real Low-Dose Data—0
Cycle-free CycleGAN using Invertible Generator for Unsupervised Low-Dose CT Denoising—0
Cycle-guided Denoising Diffusion Probability Model for 3D Cross-modality MRI Synthesis—0
Sparse Recovery and Dictionary Learning from Nonlinear Compressive Measurements—0
Sparse Representation and Non-Negative Matrix Factorization for image denoise—0
Sparse Signal Subspace Decomposition Based on Adaptive Over-complete Dictionary—0
Sparse Subspace Denoising for Image Manifolds—0
Analyzing and Improving Model Collapse in Rectified Flow Models—0
Various Total Variation for Snapshot Video Compressive Imaging—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