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 4751–4775 of 7282 papers

TitleStatusHype
MAXIM: Multi-Axis MLP for Image ProcessingCode3
Hyperspectral Image Denoising Using Non-convex Local Low-rank and Sparse Separation with Spatial-Spectral Total Variation Regularization—0
A Cross Validation Framework for Signal Denoising with Applications to Trend Filtering, Dyadic CART and Beyond—0
Block Walsh-Hadamard Transform Based Binary Layers in Deep Neural Networks—0
Deep Ultrasound Denoising Without Clean Data—0
Corrupting Data to Remove Deceptive Perturbation: Using Preprocessing Method to Improve System Robustness—0
Automatic Muscle Artifacts Identification and Removal from Single-Channel EEG Using Wavelet Transform with Meta-heuristically Optimized Non-local Means Filter—0
Image Denoising with Control over Deep Network HallucinationCode0
Fast and High-Quality Image Denoising via Malleable Convolutions—0
Learning Self-prior for Mesh Denoising using Dual Graph Convolutional NetworksCode1
TWIST: Two-Way Inter-Label Self-Training for Semi-Supervised 3D Instance SegmentationCode0
Memory-Augmented Deep Conditional Unfolding Network for Pan-SharpeningCode1
KNN Local Attention for Image Restoration—0
Neural Prior for Trajectory Estimation—0
Wnet: Audio-Guided Video Object Segmentation via Wavelet-Based Cross-Modal Denoising NetworksCode1
HLRTF: Hierarchical Low-Rank Tensor Factorization for Inverse Problems in Multi-Dimensional Imaging—0
Exploring Denoised Cross-Video Contrast for Weakly-Supervised Temporal Action Localization—0
A Low-Cost & Real-Time Motion Capture System—0
Subspace modeling for fast and high-sensitivity X-ray chemical imaging—0
Task-specific Optimization of Virtual Channel Linear Prediction-based Speech Dereverberation Front-End for Far-Field Speaker VerificationCode0
Image Edge Restoring Filter—0
Quasi-Taylor Samplers for Diffusion Generative Models based on Ideal Derivatives—0
Ultrasound Speckle Suppression and Denoising using MRI-derived Normalizing Flow Priors—0
Self-Attention Generative Adversarial Network for Iterative Reconstruction of CT Images—0
Manifold Learning Benefits GANs—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