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

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Showing 5201–5225 of 7282 papers

TitleStatusHype
ShipGen: A Diffusion Model for Parametric Ship Hull Generation with Multiple Objectives and Constraints—0
V2V3D: View-to-View Denoised 3D Reconstruction for Light Field Microscopy—0
Shot Noise Reduction in Radiographic and Tomographic Multi-Channel Imaging with Self-Supervised Deep Learning—0
Should EBMs model the energy or the score?—0
A Two-Stage Progressive Pre-training using Multi-Modal Contrastive Masked Autoencoders—0
Siamese Meets Diffusion Network: SMDNet for Enhanced Change Detection in High-Resolution RS Imagery—0
SiamTrans: Zero-Shot Multi-Frame Image Restoration with Pre-Trained Siamese Transformers—0
An Homotopy Algorithm for the Lasso with Online Observations—0
Quantum Image Denoising: A Framework via Boltzmann Machines, QUBO, and Quantum Annealing—0
An Image is Worth Multiple Words: Multi-attribute Inversion for Constrained Text-to-Image Synthesis—0
An image structure model for exact edge detection—0
AnimatableDreamer: Text-Guided Non-rigid 3D Model Generation and Reconstruction with Canonical Score Distillation—0
AnimateMe: 4D Facial Expressions via Diffusion Models—0
An Improved Variational Method for Image Denoising—0
An improved wavelet-based signal-denoising architecture with less hardware consumption—0
An incremental algorithm based on multichannel non-negative matrix partial co-factorization for ambient denoising in auscultation—0
An information theoretic approach to the autoencoder—0
An inner-loop free solution to inverse problems using deep neural networks—0
An Interpretation of Regularization by Denoising and its Application with the Back-Projected Fidelity Term—0
An intertwined neural network model for EEG classification in brain-computer interfaces—0
An Introduction to Autoencoders—0
An Investigation of Noise Robustness for Flow-Matching-Based Zero-Shot TTS—0
AniRes2D: Anisotropic Residual-enhanced Diffusion for 2D MR Super-Resolution—0
Anisotropic Diffusion Probabilistic Model for Imbalanced Image Classification—0
An Iterated L1 Algorithm for Non-smooth Non-convex Optimization in Computer Vision—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