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 5576–5600 of 7282 papers

TitleStatusHype
Noise Learning Based Denoising Autoencoder—0
Identifying First-order Lowpass Graph Signals using Perron Frobenius Theorem—0
Image Denoising using Attention-Residual Convolutional Neural Networks—0
Deep Learning Based Channel Covariance Matrix Estimation with User Location and Scene Images—0
Hyperspectral Image Denoising via Multi-modal and Double-weighted Tensor Nuclear Norm—0
Exploring Adversarial Robustness of Multi-Sensor Perception Systems in Self Driving—0
Fitting very flexible models: Linear regression with large numbers of parameters—0
Signal Processing on Higher-Order Networks: Livin' on the Edge ... and Beyond—0
Context-Aware Image Denoising with Auto-Threshold Canny Edge Detection to Suppress Adversarial Perturbation—0
BiGCN: A Bi-directional Low-Pass Filtering Graph Neural Network—0
Denoising Score Matching with Random Fourier Features—0
DAEs for Linear Inverse Problems: Improved Recovery with Provable Guarantees—0
Joint Demosaicking and Denoising in the Wild: The Case of Training Under Ground Truth Uncertainty—0
Deep Gaussian Denoiser Epistemic Uncertainty and Decoupled Dual-Attention FusionCode0
Deep Neural Networks to Recover Unknown Physical Parameters from Oscillating Time Series—0
Generate Natural Language Explanations for Recommendation—0
Interspeech 2021 Deep Noise Suppression ChallengeCode0
Contextual colorization and denoising for low-light ultra high resolution sequences—0
Prior Knowledge Input to Improve LSTM Auto-encoder-based Characterization of Vehicular Sensing Data—0
Quaternion higher-order singular value decomposition and its applications in color image processing—0
Self-Supervised Image Prior Learning With GMM From a Single Noisy ImageCode0
Differentiable Programming for Piecewise Polynomial Functions—0
Sparta: Spatially Attentive and Adversarially Robust Activations—0
Cross-Patch Graph Convolutional Network for Image Denoising—0
The Benefit of Distraction: Denoising Camera-Based Physiological Measurements Using Inverse Attention—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