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 3401–3425 of 7282 papers

TitleStatusHype
Deep MMD Gradient Flow without adversarial training—0
HORT: Monocular Hand-held Objects Reconstruction with Transformers—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
H2TF for Hyperspectral Image Denoising: Where Hierarchical Nonlinear Transform Meets Hierarchical Matrix Factorization—0
GUNet: A Graph Convolutional Network United Diffusion Model for Stable and Diversity Pose Generation—0
Guided Speech Enhancement Network—0
How Powerful is Implicit Denoising in Graph Neural Networks—0
How should a fixed budget of dwell time be spent in scanning electron microscopy to optimize image quality?—0
Deep Metric Learning-Based Out-of-Distribution Detection with Synthetic Outlier Exposure—0
How to Best Combine Demosaicing and Denoising?—0
How to Construct Energy for Images? Denoising Autoencoder Can Be Energy Based Model—0
How to monitor and mitigate stair-casing in l1 trend filtering—0
Guided Frequency Loss for Image Restoration—0
Guided Filter based Edge-preserving Image Non-blind Deconvolution—0
How to Unlock Time Series Editing? Diffusion-Driven Approach with Multi-Grained Control—0
How vulnerable is my policy? Adversarial attacks on modern behavior cloning policies—0
DeepMeshFlow: Content Adaptive Mesh Deformation for Robust Image Registration—0
Backdoor Attacks against Image-to-Image Networks—0
HSIDMamba: Exploring Bidirectional State-Space Models for Hyperspectral Denoising—0
Guided Diffusion Model for Sensor Data Obfuscation—0
Deep Manifold Prior—0
Guided Conditional Diffusion Classifier (ConDiff) for Enhanced Prediction of Infection in Diabetic Foot Ulcers—0
Deeply Coupled Auto-encoder Networks for Cross-view Classification—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