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 3576–3600 of 7282 papers

TitleStatusHype
Adversarial Signal Denoising with Encoder-Decoder Networks—0
Denoising Concept Vectors with Sparse Autoencoders for Improved Language Model Steering—0
Image Segmentation Using Overlapping Group Sparsity—0
A Coordinate Descent Approach to Atomic Norm Denoising—0
Energy-Based Processes for Exchangeable Data—0
Image Tag Completion by Low-rank Factorization with Dual Reconstruction Structure Preserved—0
Denoising Criterion for Variational Auto-Encoding Framework—0
Contrastive CFG: Improving CFG in Diffusion Models by Contrasting Positive and Negative Concepts—0
Inverse Flow and Consistency Models—0
End-to-End Unsupervised Document Image Blind Denoising—0
End-to-end Triple-domain PET Enhancement: A Hybrid Denoising-and-reconstruction Framework for Reconstructing Standard-dose PET Images from Low-dose PET Sinograms—0
Imagine Flash: Accelerating Emu Diffusion Models with Backward Distillation—0
Denoising Dictionary Learning Against Adversarial Perturbations—0
Imaging Transformer for MRI Denoising: a Scalable Model Architecture that enables SNR << 1 Imaging—0
Denoising diffusion algorithm for inverse design of microstructures with fine-tuned nonlinear material properties—0
Contrastive-Adversarial and Diffusion: Exploring pre-training and fine-tuning strategies for sulcal identification—0
End-to-end Recurrent Denoising Autoencoder Embeddings for Speaker Identification—0
Impact of Benign Modifications on Discriminative Performance of Deepfake Detectors—0
Impact of Bottleneck Layers and Skip Connections on the Generalization of Linear Denoising Autoencoders—0
A Review Paper: Noise Models in Digital Image Processing—0
Imperceptible Adversarial Attacks on Point Clouds Guided by Point-to-Surface Field—0
End-to-End Learning for Structured Prediction Energy Networks—0
Denoising diffusion-based synthetic generation of three-dimensional (3D) anisotropic microstructures from two-dimensional (2D) micrographs—0
Continuous-variable Quantum Diffusion Model for State Generation and Restoration—0
3D Wasserstein generative adversarial network with dense U-Net based discriminator for preclinical fMRI denoising—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