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 3451–3475 of 7282 papers

TitleStatusHype
Enhancement of the Prefiltered Rotationally Invariant Non-local PCA Algorithm for MRI—0
Hyper-Restormer: A General Hyperspectral Image Restoration Transformer for Remote Sensing Imaging—0
Adversary-Robust Graph-Based Learning of WSIs—0
Hyperspectral Denoising Using Unsupervised Disentangled Spatio-Spectral Deep Priors—0
Improving the quality control of seismic data through active learning—0
Impulse Denoising From Hyper-Spectral Images: A Blind Compressed Sensing Approach—0
Incorporating Broad Phonetic Information for Speech Enhancement—0
Hyperspectral Image Denoising Based On Multi-Stream Denoising Network—0
Hyperspectral Image Denoising Using Non-convex Local Low-rank and Sparse Separation with Spatial-Spectral Total Variation Regularization—0
Enhancement of land-use change modeling using convolutional neural networks and convolutional denoising autoencoders—0
Enhancement of a CNN-Based Denoiser Based on Spatial and Spectral Analysis—0
Hyperspectral Image Denoising via Global Spatial-Spectral Total Variation Regularized Nonconvex Local Low-Rank Tensor Approximation—0
Enhanced segmentation of femoral bone metastasis in CT scans of patients using synthetic data generation with 3D diffusion models—0
Hyperspectral Image Denoising with Log-Based Robust PCA—0
Controllable Diverse Sampling for Diffusion Based Motion Behavior Forecasting—0
Controllable Confidence-Based Image Denoising—0
Enhanced Low-Rank Matrix Approximation—0
Hyperspectral Image Recovery via Hybrid Regularization—0
Hyperspectral Image Denoising via Multi-modal and Double-weighted Tensor Nuclear Norm—0
DEL-Ranking: Ranking-Correction Denoising Framework for Elucidating Molecular Affinities in DNA-Encoded Libraries—0
Hyperspectral Mixed Noise Removal via Subspace Representation and Weighted Low-rank Tensor Regularization—0
A Robust Completed Local Binary Pattern (RCLBP) for Surface Defect Detection—0
I2V: Towards Texture-Aware Self-Supervised Blind Denoising using Self-Residual Learning for Real-World Images—0
A Magnetic Framelet-Based Convolutional Neural Network for Directed Graphs—0
Enhanced Denoising of Optical Coherence Tomography Images Using Residual U-Net—0
Show:102550
← PrevPage 139 of 292Next →
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