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 4251–4275 of 7282 papers

TitleStatusHype
Hyperspectral Image Denoising Using Non-convex Local Low-rank and Sparse Separation with Spatial-Spectral Total Variation Regularization—0
VODiff: Controlling Object Visibility Order in Text-to-Image Generation—0
Hyperspectral Image Denoising via Global Spatial-Spectral Total Variation Regularized Nonconvex Local Low-Rank Tensor Approximation—0
Hyperspectral Image Denoising with Log-Based Robust PCA—0
Hyperspectral Image Denoising with Partially Orthogonal Matrix Vector Tensor Factorization—0
Volume Tells: Dual Cycle-Consistent Diffusion for 3D Fluorescence Microscopy De-noising and Super-Resolution—0
Hyperspectral Image Recovery via Hybrid Regularization—0
Hyperspectral Image Denoising via Multi-modal and Double-weighted Tensor Nuclear Norm—0
Hyperspectral Mixed Noise Removal via Subspace Representation and Weighted Low-rank Tensor Regularization—0
Texture Aware Autoencoder Pre-training And Pairwise Learning Refinement For Improved Iris Recognition—0
I2V: Towards Texture-Aware Self-Supervised Blind Denoising using Self-Residual Learning for Real-World Images—0
IC3D: Image-Conditioned 3D Diffusion for Shape Generation—0
I Can See Clearly Now : Image Restoration via De-Raining—0
ICDM: Interference Cancellation Diffusion Models for Wireless Semantic Communications—0
Textured 3D Regenerative Morphing with 3D Diffusion Prior—0
Texture Enhanced Image Denoising via Gradient Histogram Preservation—0
Identification and Correction of False Data Injection Attacks against AC State Estimation using Deep Learning—0
Identifying Arrhythmias Based on ECG Classification Using Enhanced-PCA and Enhanced-SVM Methods—0
Identifying First-order Lowpass Graph Signals using Perron Frobenius Theorem—0
ADT: Tuning Diffusion Models with Adversarial Supervision—0
ID-Pose: Sparse-view Camera Pose Estimation by Inverting Diffusion Models—0
2nd Place Solutions for UG2+ Challenge 2022 -- D^3Net for Mitigating Atmospheric Turbulence from Images—0
IDVT: Interest-aware Denoising and View-guided Tuning for Social Recommendation—0
IKDP: Inverse Kinematics through Diffusion Process—0
Discovering Structure From Corruption for Unsupervised Image Reconstruction—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