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 2301–2350 of 7282 papers

TitleStatusHype
Balancing Act: Distribution-Guided Debiasing in Diffusion Models—0
Deep Noise Suppression With Non-Intrusive PESQNet Supervision Enabling the Use of Real Training Data—0
Deep Noise Suppression Maximizing Non-Differentiable PESQ Mediated by a Non-Intrusive PESQNet—0
Bag of Tricks for Effective Language Model Pretraining and Downstream Adaptation: A Case Study on GLUE—0
Deep Neural Networks to Recover Unknown Physical Parameters from Oscillating Time Series—0
BADGR: Bundle Adjustment Diffusion Conditioned by GRadients for Wide-Baseline Floor Plan Reconstruction—0
Back to Basics: Fast Denoising Iterative Algorithm—0
Deep neural networks for learning graph representations—0
Deep neural networks-based denoising models for CT imaging and their efficacy—0
All-in-One Deep Learning Framework for MR Image Reconstruction—0
Enhancement of the Prefiltered Rotationally Invariant Non-local PCA Algorithm for MRI—0
Enhancing Diffusion Models for Inverse Problems with Covariance-Aware Posterior Sampling—0
Enhancing Label-efficient Medical Image Segmentation with Text-guided Diffusion Models—0
Deep neural network for fringe pattern filtering and normalisation—0
Deep Neural Network-Based Quantized Signal Reconstruction for DOA Estimation—0
Deep network series for large-scale high-dynamic range imaging—0
Deep Networks as Denoising Algorithms: Sample-Efficient Learning of Diffusion Models in High-Dimensional Graphical Models—0
Backpropagation with N-D Vector-Valued Neurons Using Arbitrary Bilinear Products—0
Deep Network for Simultaneous Decomposition and Classification in UWB-SAR Imagery—0
Deep Network for Capacitive ECG Denoising—0
Adaptive dropout for training deep neural networks—0
Deep Multi-contrast Cardiac MRI Reconstruction via vSHARP with Auxiliary Refinement Network—0
Deep MMD Gradient Flow without adversarial training—0
Background Denoising for Ptychography via Wigner Distribution Deconvolution—0
Accelerated first-order primal-dual proximal methods for linearly constrained composite convex programming—0
Enhanced Low-Rank Matrix Approximation—0
Deep Metric Learning-Based Out-of-Distribution Detection with Synthetic Outlier Exposure—0
DeepMeshFlow: Content Adaptive Mesh Deformation for Robust Image Registration—0
Backdoor Attacks against Image-to-Image Networks—0
Generative Imaging and Image Processing via Generative Encoder—0
Deep Manifold Prior—0
Deeply Coupled Auto-encoder Networks for Cross-view Classification—0
Poisson-Gaussian Holographic Phase Retrieval with Score-based Image Prior—0
Deeply Cascaded U-Net for Multi-Task Image Processing—0
Deeply Aggregated Alternating Minimization for Image Restoration—0
Aligning Protein Conformation Ensemble Generation with Physical Feedback—0
Adaptive Domain Learning for Cross-domain Image Denoising—0
Enhanced segmentation of femoral bone metastasis in CT scans of patients using synthetic data generation with 3D diffusion models—0
Deep Learning with robustness to missing data: A novel approach to the detection of COVID-19—0
AWCD: An Efficient Point Cloud Processing Approach via Wasserstein Curvature—0
Enhanced Confocal Laser Scanning Microscopy with Adaptive Physics Informed Deep Autoencoders—0
Deep Learning on Image Denoising: An overview—0
Avoiding mode collapse in diffusion models fine-tuned with reinforcement learning—0
Enhanced DACER Algorithm with High Diffusion Efficiency—0
Deep-Learning Inversion Method for the Interpretation of Noisy Logging-While-Drilling Resistivity Measurements—0
A Variational Step for Reduction of Mixed Gaussian-Impulse Noise from Images—0
Deep Learning Improves Contrast in Low-Fluence Photoacoustic Imaging—0
Deep Learning Hyperspectral Image Classification Using Multiple Class-based Denoising Autoencoders, Mixed Pixel Training Augmentation, and Morphological Operations—0
Aligning Foundation Model Priors and Diffusion-Based Hand Interactions for Occlusion-Resistant Two-Hand Reconstruction—0
Enhanced Denoising and Convergent Regularisation Using Tweedie Scaling—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