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 5051–5075 of 7282 papers

TitleStatusHype
Efficient automated U-Net based tree crown delineation using UAV multi-spectral imagery on embedded devices—0
Beyond In-Place Corruption: Insertion and Deletion In Denoising Probabilistic Models—0
Spectral Processing and Optimization of Static and Dynamic 3D Geometries—0
HTLM: Hyper-Text Pre-Training and Prompting of Language Models—0
AdvFilter: Predictive Perturbation-aware Filtering against Adversarial Attack via Multi-domain Learning—0
Meta-Optimization of Deep CNN for Image Denoising Using LSTM—0
Project Achoo: A Practical Model and Application for COVID-19 Detection from Recordings of Breath, Voice, and Cough—0
Perceptual-based deep-learning denoiser as a defense against adversarial attacks on ASR systems—0
Sentinel-1 Additive Noise Removal from Cross-Polarization Extra-Wide TOPSAR with Dynamic Least-Squares—0
Detect and Defense Against Adversarial Examples in Deep Learning using Natural Scene Statistics and Adaptive DenoisingCode0
Denoising User-aware Memory Network for Recommendation—0
R3L: Connecting Deep Reinforcement Learning to Recurrent Neural Networks for Image Denoising via Residual Recovery—0
Details Preserving Deep Collaborative Filtering-Based Method for Image Denoising—0
Hierarchical Learning Framework for UAV Detection and Identification—0
Dense-Sparse Deep Convolutional Neural Networks Training for Image Denoising—0
Collaborative Filtering-Based Method for Low-Resolution and Details Preserving Image Denoising—0
Wavelet Transform-assisted Adaptive Generative Modeling for ColorizationCode1
Retinal OCT Denoising with Pseudo-Multimodal Fusion Network—0
Incorporating Multi-Target in Multi-Stage Speech Enhancement Model for Better Generalization—0
Structured Denoising Diffusion Models in Discrete State-SpacesCode2
Deep Unfolding with Normalizing Flow Priors for Inverse Problems—0
Deep Mesh Prior: Unsupervised Mesh Restoration using Graph Convolutional NetworksCode1
Deep learning-based statistical noise reduction for multidimensional spectral data—0
On Measuring and Controlling the Spectral Bias of the Deep Image PriorCode1
Inter-Beat Interval Estimation with Tiramisu Model: A Novel Approach with Reduced ErrorCode0
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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