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 2301–2325 of 7282 papers

TitleStatusHype
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
End-to-End Learning for Structured Prediction Energy Networks—0
Energy-Based Processes for Exchangeable Data—0
Enhanced channel estimation for near-field IRS-aided multi-user MIMO system via a large deep residual network—0
Enhanced Low-Rank Matrix Approximation—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
END4Rec: Efficient Noise-Decoupling for Multi-Behavior Sequential Recommendation—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