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 6151–6175 of 7282 papers

TitleStatusHype
Learned Semantic Multi-Sensor Depth Map Fusion—0
Credit Card Fraud Detection Using Autoencoder Neural NetworkCode0
Learning a Multi-Domain Curriculum for Neural Machine Translation—0
Complex Deep Learning Models for Denoising of Human Heart ECG signals—0
Automatic Detection of ECG Abnormalities by using an Ensemble of Deep Residual Networks with Attention—0
Nearest Neighbor Search-Based Bitwise Source Separation Using Discriminant Winner-Take-All Hashing—0
Deep Camera: A Fully Convolutional Neural Network for Image Signal Processing—0
Denoising based Sequence-to-Sequence Pre-training for Text Generation—0
Signal denoising based on the Schr\"odinger operator's eigenspectrum and a curvature constraint—0
Denoising and Verification Cross-Layer Ensemble Against Black-box Adversarial Attacks—0
Spatio-temporal Manifold Learning for Human Motions via Long-horizon Modeling—0
A Blind Multiscale Spatial Regularization Framework for Kernel-based Spectral Unmixing—0
Convolutional Neural Network with Median Layers for Denoising Salt-and-Pepper ContaminationsCode0
Multi-Kernel Filtering for Nonstationary Noise: An Extension of Bilateral Filtering Using Image Context—0
A Comparative Study of Filtering Approaches Applied to Color Archival Document Images—0
On Convex Duality in Linear Inverse Problems—0
Fast Sub-millimeter Diffusion MRI using gSlider-SMS and SNR-Enhancing Joint Reconstruction—0
DAPAS : Denoising Autoencoder to Prevent Adversarial attack in Semantic Segmentation—0
Index NetworkCode0
PCGAN-CHAR: Progressively Trained Classifier Generative Adversarial Networks for Classification of Noisy Handwritten Bangla Characters—0
Bias and variance reduction and denoising for CTF Estimation—0
WhiteNNer-Blind Image Denoising via Noise Whiteness Priors—0
Manifold Modeling in Embedded Space: A Perspective for Interpreting Deep Image PriorCode0
Defending Against Adversarial Iris Examples Using Wavelet Decomposition—0
CameraNet: A Two-Stage Framework for Effective Camera ISP Learning—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