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 3376–3400 of 7282 papers

TitleStatusHype
Deep Neural Network-Based Quantized Signal Reconstruction for DOA Estimation—0
HanDrawer: Leveraging Spatial Information to Render Realistic Hands Using a Conditional Diffusion Model in Single Stage—0
Handling Noise in Single Image Deblurring Using Directional Filters—0
Deep network series for large-scale high-dynamic range imaging—0
Handling noise in image deblurring via joint learning—0
Handling Background Noise in Neural Speech Generation—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
Handheld Burst Super-Resolution Meets Multi-Exposure Satellite Imagery—0
Deep Network for Simultaneous Decomposition and Classification in UWB-SAR Imagery—0
HandCT: hands-on computational dataset for X-Ray Computed Tomography and Machine-Learning—0
Deep Network for Capacitive ECG Denoising—0
Hair and Scalp Disease Detection using Machine Learning and Image Processing—0
D-SCo: Dual-Stream Conditional Diffusion for Monocular Hand-Held Object Reconstruction—0
Deep Multi-contrast Cardiac MRI Reconstruction via vSHARP with Auxiliary Refinement Network—0
Haar Nuclear Norms with Applications to Remote Sensing Imagery Restoration—0
Deep MMD Gradient Flow without adversarial training—0
HLRTF: Hierarchical Low-Rank Tensor Factorization for Inverse Problems in Multi-Dimensional Imaging—0
Background Denoising for Ptychography via Wigner Distribution Deconvolution—0
Adaptive dropout for training deep neural networks—0
H3AE: High Compression, High Speed, and High Quality AutoEncoder for Video Diffusion Models—0
Holistic Processing of Colour Images Using Novel Quaternion-Valued Wavelets on the Plane—0
Holographic Neural Architectures—0
Homodyned K-Distribution Parameter Estimation in Quantitative Ultrasound: Autoencoder and Bayesian Neural Network Approaches—0
H2TF for Hyperspectral Image Denoising: Where Hierarchical Nonlinear Transform Meets Hierarchical Matrix Factorization—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